Assessment of Drought Tolerance in Wheat genotypes for Sustainable Food Security and Breeding Programs

Assessment of Drought Tolerance in Wheat genotypes for Sustainable Food Security and Breeding Programs | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Assessment of Drought Tolerance in Wheat genotypes for Sustainable Food Security and Breeding Programs Adnan Noor Shah, Hafiz Ghulam, Muhammad Naeem, Aziz Ullah, Amjad Saeed, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4001110/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The crop which is widely consumed and utilized because of its social benefits and economic importance worldwide is wheat. In many parts of the world, it is generally consumed as feed and food for its nutritional benefits, and its productivity is uniform under different environmental effects. In a randomized complete block design (RCBD) with three replication, an experiment was conducted to assess 50 different wheat cultivars. These cultivars were evaluated under two distinct levels; normal (non-stressed) and drought (stressed) conditions. The analysis of variance (ANOVA) under non-stressed (normal) and stressed (drought) conditions of the examined traits showed highly significant differences which indicated the variation in the studied germplasm. The Genotypes that performed very well in both the normal and water deficit conditions are G12 followed by G8, G23, G20 and G16 while genotypes that performed poorly in both conditions were G35 followed by, G37, G47, G48 and G42 considered as drought susceptible genotypes. Attribute grain yield per plant disclosed the significant and positive correlation among all the studied traits under the non-stresses condition. Results revealed from principal component analysis (PCA), only 4 PCA showed the significant value under the both normal and stressed conditions because these PCAs exhibited eigenvalue more than one considered as significant. Under the normal condition, the PC1 indicated the 78.7% for the variance, PC2 indicated 14.6%, PC3 indicated 12.2% and PC4 indicated 10.1% as mention in Fig-4.7. In the drought condition, the PC1 represented for 74.5%, PC2 represented for 16.7%, PC3 represented for 11.6% and PC4 represented for 11.1%. The findings from this study will be valuable for both researchers anf farmers, as they can utilize the data to cultivate these specific genotypes for improved yield. Morever, the results can also be employed in future breeding aim to develop drought-resistant wheat genotypes, contributing to sustainable food security efforts. drought grains yield association wheat Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction The most important cultivated crop wheat ( Triticumaestivum ) belongs to the cereal grass family Poaceae . Genus Triticum is comprised of many species out of which two major species are Triticumaestivum and Triticum durum . Globally wheat is consumed as a staple food which is majorly cultivated around most of the earth’s areas and requires consideration because of its significance for long-term food security. Around 30% of the world’s surface is covered with wheat (Pocketbook 2015) and it ranks at 2nd position in terms of production after maize crop (Cossani and Reynolds 2012 ). A minimal decrease in the cultivated area as compared to the last year in Pakistan, the production of the wheat crop is set to reach about 27 million tons in the year 2023-24, which will be 2% higher than the last growing season (Panneer et al. 2023). Yet, demand for wheat is increasing as well and is expected to keep doing so at an annual average of approximately about 2% (Jaradat 2011). The occurrence and severity of drought are predicted to increase in coming years as a result of the current climate change which will have significant impact on the patterns of precipitation and temperature (Ergen and Budak 2009 ). Most significant abiotic stress factor which is causing agricultural yield reductions is drought, hence high-yielding crops are important even in ecologically stressed situations (Fleury et al. 2010 ). The situation is not new as the demand is increasing annually due to increase in the population pressure, similar situation was also faced by the world which was mitigated by genetically modified high grain yielding wheat crop 50 years ago which coined as Green Revolution (Tester and Langridge 2010). Tolerance for the drought is intricate with environmental interactions. The evaluation of response in drought environment by plant, its mode and time of response is directly related to the extremity of the cell dehydration during the stress period and the significance of its interaction with both abiotic and biotic stress factors (Ribaut 2006). The breeding for the drought resistant varieties is a complicated process as many other environmental factors are also involved and compete with the crop plants altogether. The factors like extreme temperature, light intensity, shortage of water and deficiency of nutrients generally effects the normal growing condition but cannot be manageable through conventional farm procedures. Some soil may exhibit certain properties which can also affect the balance of different environmental factors which can be composition and structure (Whitmore and Whalley 2009). The rate by which the yield is increasing is still low to meet the demands of wheat 2050 (Mwadzingeni et al. 2016). The duration and impact of the water scarcity can determine the loss of yield by decreasing the life cycle and time needed for proper grain filling (Farooq et al. 2014). The rate of transpiration is significantly decreased when plants face water shortage, due to closure of stomata the heat is trapped and leaves temperature is increased, it leads to the disruption of the physiological processes happening in the plant (Manunta et al. 2002). The attributes that influence the grain yield of the wheat cultivars are number of panicles, grain weight and biological yield (Leilah and Al-Khateeb 2005). Considering the limitations posed by climate change and water scarcity, it will be important to improve biomass production and economic yield (Spiertz 2013). The major aim for the current study is to improve and develop wheat cultivars under the drought stress and to increase the yield potential of the wheat. The 50 studied genotypes were selected to evaluate with Pearson’s correlation and PCA (principal component analysis) under the stressed and non-stressed conditions. Methodology The present study was took place in the arid region of the Punjab, specifically at the Department of Plant Breeding and Genetics, The Islamia University of Bahawalpur. Randomized Complete Block design (RCBD) was applied to 50 wheat genotypes and were evaluated in three replications under non-stressed (normal) irrigated and stressed (less water) drought condition during the cropping year 2022–2023. 3 seeds were sown and later thinning was done to evaluate a single plant. The row-to-row distance was 12 inches. The normal field was irrigated 5 times until the maturity while the drought stresses plot was watered only once at the flowering stage (Anthesis) and no rainfall was reported during growing season. Rate at which the fertilizer was applied was 150:100:50 kilograms N: P: K per hectare which was enough to achieve maximum crop potential. The attributes which were recorded were Plant Height (PH), Flag Leaf Area (FLA), Tillers per plant (TP), Peduncle length (PL), Days to heading (DH), Days to maturity (DM), Spike Length (SL), Spikelet per Spike (SS), Number of grains per spike (NGS), 1000-grain weight (TGW), Grain yield per plant (GYP), Biological yield per plant (BYP) Harvest index (HI). Analysis of variance and Pearson’s correlation was calculated from the collected data using the software statistix 8.1. The correlation cluster map was developed using R-studio and Principle component analysis using XLstat. Results and Discussion The ANOVA (Analysis Of Variance) presented in the Table 1 which indicated significant difference between the treatments and genotypes. Table 1 also represented the significant interaction among the studied genotypes and the treatment. The Genotypes that performed very well in both the normal and water deficit conditions are G12 followed by G8, G23, G20 and G16 and indicated as genotypes with drought tolerance while the cultivars that performed poorly under both conditions were G35 followed by, G37, G47, G48 and G42 considered as drought susceptible genotypes. Table 1 Analysis of variance for studied attributes under normal and drought conditions of 50 wheat genotypes. SOV Replication Genotypes Treatment Genotypes*Treatment Error Total DF 2 49 1 49 198 299 PH 0.01 12.98** 6709.79** 19.5** 0.08 FLA 0.002 4.409** 864.13** 2.62** 0.004 TPP 0.00457 0.118** 0.7834** 0.1653** 0.00265 PL 0.0111 0.355** 96.141** 0.4403** 0.0062 DTH 0.073 2.43** 875.86** 2.098** 0.022 DTM 0.00143 5.770** 2122.57** 4.6351** 0.0112 SL 18.5 69.7** 20003.4** 78.4** 18 SPS 0.03 7.52** 1012.37** 5.77** 0.01 NGS 0.81 9.62** 3310.71** 11.95** 0.6 TGW 0.058 0.425** 240.263** 0.473** 0.091 GYP 0.0028 0.343** 94.7532** 0.4332** 0.0035 BYP 0.02418 73.35** 29024.3** 53.7684** 0.01477 HI 0.052 0.26** 248.084** 0.218** 0.008 Sov (source of variance), DF (degree of freedom), ** (highly significant), Plant Height (PH), Flag Leaf Area (FLA), Tillers per plant (TP), Peduncle length (PL), Days to heading (DH), Days to maturity (DM), Spike Length (SL), Spikelet per Spike (SS), Number of grains per spike (NGS), 1000-grain weight (TGW), Grain yield per plant (GYP), Biological yield per plant (BYP) Harvest index (HI) Pearson’s Correlation Analysis for the studied attribute In the both conditions, the flag leaf area and plant height displayed highly significant and positive relationship with FLA, TP, PL, BYP, SL, SPS, NGS, 1000-GW and GYP as mentioned in Table 2 and represented in Figs. 1 & 2 . Different scientists also reported the same results. Lack of availability of water resulted in a decline in photosynthesis and uptake of other essential nutrients that subsequently reduces plant height (Sarto et al. 2017). Similar findings on reduction of the trait plant height under drought stressed environment were reported (Naghavi et al. 2015). Under the normal environmental condition significant and positive association of flag leaf length with plant height and peduncle length was reported(Amiri et al. 2013 ). A recent study concluded more the area of the flag leaf increase the levels of photosynthesis in the plant. Flag leaf area is significantly associated with tolerance for drought and is an important component of yield (Yani and Rashidi 2012). In both normal non-stressed environment and stressed drought conditions, the tillers per plant indicated that there was a strong and positive association that is statistically significant with PL, BYP, SL, SPS, NGS, 1000-GW and GYP as showed in Table 2 and Figs. 1 & 2 . Under the both normal and drought condition, tillers per plant showed positive association with all the attributes (Zahra et al. 2021 ). Recovery duration decrease the number of tillers under water stress condition and overall result revealed lower number of tillers (Ding et al. 2018 ). The peduncle length showed positive and highly significant relationship with the biological yield per plant, spike length, spikelet per spike, number of grains per spike under both mentioned environments and thousand grain weight had highly significant association only under normal condition. The peduncle length showed significant and positive correlation with grain yield per plant as mentioned in Table 2 and expressed in Figs. 1 & 2 . Similar results under the normal (non-stressed) condition were confirmed by other researcher (Khan et al. 2010). Under the non-stressed and stressed conditions, the biological yield per plant associated positively and highly significant with studied attributes like spike length, spikelet per spike, number of grains per spike, and grain yield per plant. as displayed in Table 2 and also expressed in Figs. 1 & 3 . Biological yield per plant (BYP) observed positive and significant association with 1000-grain weight under drought stress while it showed highly significant association under normal condition Table 2 and Figs. 1 & 4. These traits were positively related and have the significant correlation between them. These results were matched with research report (Peymaninia et al. 2012). Days to maturity and days to heading correlated highly significant and positively with each other while these attributes displayed negative but high significant correlation with other attributes like spike length, spikelet per spike, number of grains per spike and thousand grain weight had negative and highly significant association with days to heading. As displayed in Table 2 and in Figs. 1 & 2 . The days to heading had negative relation with all studied traits except day to maturity which was also confirmed by the researcher (Barutcular et al. 2016 ). These results also matched with the research of (Zafarnaderi et al. 2013). The spike length exhibited the correlation that was highly significant and positive with all the studied attributes like SPS, NGS, 1000GW and GYP under irrigated and non-irrigated conditions. The harvest index that mentioned significant association with spike length under normal irrigated condition and non-significant correlation under drought condition as showed in Table 2 and in Figs. 1 & 2 . Spike length had the positive association with the biological yield that was also found by (Peymaninia et al. 2012). Similar finding followed by (Slafer and Andrade 1993) that under water deficit condition spike length and grain yield correlated significantly. Under the non-stressed and stressed conditions, the spikelet per spike mentioned positively highly significant relation with the number of grains per spike, 1000-grain weight and grain yield per plant as showed in Table 2 and in Fig. 1 –4. Similar results explained by (Baloch et al. 2021 ). Positive and significant result showed by spikelet per spike with the spike length (Ajmal et al. 2013 ). The number of grains per spike associated positively and highly significant with 1000-GW and GYP. The spikelet per spike also exhibited negatively non-significant association with harvest index as expressed in Table 2 and in Figs. 1 & 2 under the normal and stressed conditions. Under the normal irrigated condition, the thousand grain weight exhibited the highly significant and positively associated with harvest index and grain yield per plant had negative and significant relationship with it as mentioned in Table 2 . Under the water deficit condition, the highly significant and positive association showed by the grain yield per plant and HI had positive and non-significant association with 1000-grain weight as represented in Table 2 and in Figs. 1 & 2 . These results collaborated with the results of research by the researcher (Ahamed et al. 2018 ). These findings also matched with the result of research by the researcher (Mollasadeghi et al. 2011). He observed in his experiment that 1000-grain weight had positive association with grain yield. The grain yield per plant mentioned the non-significant and positive relation with harvest index under the non-stressedl condition as showed in Table 2 and Figs. 1 & 2 while GYP correlated hghly significant and positively with harvest index (0.69**) under the stressed condition. Grain yield per spike was the positively and significant associated with the grain yield per plant. Both traits were positively connected. These observations were agreed with the result of researcher (Nasri et al. 2014). The similar findings also matched with the results of the research by the researcher (Abderrahmane et al. 2013). The traits like (FLA), (TP), (PH), (PL), (SL), (BYP), (SPS), (NGPS) and 1000-GW had negative association with the HI under irrigated condition while under low water condition harvest index had positive but non-significant association with studied traits as displayed in Table 2 and Figs. 1 & 2 . Table 2 Correlation studies among 13 yield related traits under stressed and non-stressed conditions in 50 wheat genotypes Traits Conditions PH FLA TPP PL BYP DH DM SL SPS NGPS TGW GYP FLA Normal 0.79** FLA Drought 0.81** TPP Normal 0.84** 0.78** TPP Drought 0.63** 0.71** PL Normal 0.84** 0.77** 0.87** PL Drought 0.87** 0.79** 0.7** BYP Normal 0.76** 0.7** 0.87** 0.84** BYP Drought 0.65** 0.55** 0.54** 0.77** DH Normal -0.55 -0.6 -0.39 -0.5 -0.4 DH Drought -0.48 -0.46 -0.41 -0.45 -0.21 DM Normal -0.54 -0.58 -0.36 -0.49 -0.38 0.97 DM Drought -0.48 -0.46 -0.41 -0.45 -0.21 1** SL Normal 0.8** 0.74** 0.83** 0.88** 0.91** -0.5 -0.47 SL Drought 0.71** 0.62** 0.56** 0.76** 0.64** -0.59 -0.59 SPS Normal 0.72** 0.66** 0.54** 0.66** 0.58** -0.52 -0.54 0.7** SPS Drought 0.64** 0.63** 0.4** 0.7** 0.35** -0.52 -0.52 0.56** NGPS Normal 0.56** 0.5** 0.36** 0.47** 0.43** -0.46 -0.5 0.56** 0.85** NGPS Drought 0.59** 0.57** 0.41** 0.66** 0.36 -0.49 -0.49 0.53** 0.82** TGW Normal 0.71** 0.68** 0.54** 0.64** 0.58** -0.51 -0.53 0.7** 0.98** 0.82** TGW Drought 0.63** 0.63** 0.39** 0.67** 0.34* -0.5 -0.5 0.54** 0.98** 0.8** GYP Normal 0.56** 0.42** 0.34* 0.34* 0.43** -0.23 -0.22 0.49** 0.59** 0.42** 0.6** GYP Drought 0.76** 0.62** 0.65** 0.74** 0.69** -0.34 -0.34 0.63** 0.46** 0.49** 0.44** HI Normal -0.3 -0.28 -0.23 -0.35 -0.26 0.09ns 0.1ns -0.3 -0.28 -0.27 -0.29 0.01ns HI Drought 0.2ns 0.08ns 0.13ns 0.19ns 0.23ns -0.13 -0.13 0.18ns 0.17ns 0.21ns 0.15ns 0.69** Plant Height (PH), Flag Leaf Area (FLA), Tillers per plant (TP), Peduncle length (PL), Days to heading (DH), Days to maturity (DM), Spike Length (SL), Spikelet per Spike (SS), Number of grains per spike (NGS), 1000-grain weight (TGW), Grain yield per plant (GYP), Biological yield per plant (BYP) Harvest index (HI) PRINCIPAL COMPONENT ANALYSIS In order to explain the variability and relationship of wheat attributes under normal and drought environments, the variations were mentioned in studied 50 wheat genotypes that based on correlation by using principal component analysis (PCA). In the studied genotypes, only 4 attributes showed the significant value under the both non-stressed and stressed conditions as mentioned in Table 3 . The trait that exhibited eigenvalue more than one considered as significant. The eigenvalue that showed value less than one considered as non-significant. The 4 traits observed significant eigenvalue in the normal and drought environments, while other traits did not exhibit significant values. The statistically significant principal components were selected based on significant eigenvalues which was mentioned by (Kaiser 1960) The PCs that had eigenvalues higher than one were considered as significant. Under the normal condition, the PC1 indicated the 78.7% for the variance, PC2 indicated 14.6%, PC3 indicated 12.2% and PC4 indicated 10.1%. In the drought condition, the PC1 assumed for 74.5%, PC2 assumed for 16.7%, PC3 assumed for 11.6% and PC4 assumed for 11.1% as showed in Table 3 In breeding procedures for selection of parents biplot analysis was used previously (Sisodia and Rai 2017). In order to chose various parents for breeding procedures and hybridization, principal component analysis is very useful (Khodadadi et al. 2011). Table 3 Eigen analysis of 13 yield and yield related indices under irrigated and stressed conditions Env. Eigenvalue Proportion Cumulative Normal Drought Normal Drought Normal Drought PC1 7.87 7.45 0.61 0.57 0.61 0.57 PC2 1.46 1.67 0.11 0.13 0.72 0.70 PC3 1.22 1.16 0.09 0.09 0.81 0.79 PC4 1.01 1.11 0.08 0.09 0.89 0.88 PC5 0.49 0.52 0.04 0.04 0.93 0.92 PC6 0.32 0.32 0.03 0.02 0.95 0.94 PC7 0.20 0.25 0.02 0.02 0.97 0.96 PC8 0.18 0.23 0.01 0.02 0.98 0.98 PC9 0.11 0.17 0.01 0.01 0.99 0.99 PC10 0.06 0.08 0.01 0.01 0.99 1.00 PC11 0.05 0.03 0.00 0.00 1.00 1.00 PC12 0.02 0.01 0.00 0.00 1.00 1.00 PC13 0.01 0.00 0.00 0.00 1.00 1.00 Under the normal condition, the result of first PC showed that it related highly with PH (0.32), PL (0.32) and SL (0.32) while it was negatively related with the DTH (-0.24), DTM (-0.24) and HI (-0.12). The second PC indicated that it highly related with the number of grains per spike (0.31) and spikelets per spike (0.21) but it showed negative relation with all studied traits like PH (-0.12), FLA (-0.06), PL (-0.25) tillers per plant (-0.38) and biological yield per plant (-0.33) as mentioned in Table # 4. The third PC observed that it was highly related with grain yield per plant (0.46),DTH (0.45), DTM (0.42) and number of grains per spike (0.34) and it was negatively related with PH (-0.03), biological yield per plant (-0.08), HI (-0.09), FLA (-0.14) and tillers per plant (-0.17). The result of forth PC presented that it was positively related with HI (0.85), GYP (0.44), TP (0.12) and BYP (0.12) while it was negatively related with DTH (-0.01), PL (-0.04), TGW (-0.07) and NGS (-0.19) as showed in Table 4 . The shortage of water causes loss of water from plant tissues, which causes the disruption of both membrane structure and function. The first organ to be damaged due to the water shortage in plant is plant cell membrane, the plant potential against drought determines the sustainability and integrity of plant under drought conditions. This is the basic criteria to be used for the discrimination against tolerant and susceptible wheat varieties (Dhanda et al. 2004 ). Table 4 PCA Variability of 13 yield and yield related traits under normal and low water conditions Variable PC1 PC2 PC3 PC4 PH 0.32 -0.12 -0.03 0.08 PH 0.32 -0.12 0.12 -0.07 FLA 0.31 -0.06 -0.14 0.02 FLA 0.31 -0.05 0.24 -0.13 TPP 0.29 -0.38 -0.17 0.12 TPP 0.26 -0.19 0.07 -0.33 PL 0.32 -0.25 -0.15 -0.04 PL 0.34 -0.15 0.18 -0.04 DH -0.24 -0.44 0.45 -0.01 DH -0.24 -0.41 0.39 0.29 DM -0.24 -0.47 0.42 0.03 DM -0.24 -0.41 0.39 0.29 SL 0.32 -0.21 -0.03 0.06 SL 0.3 -0.03 -0.06 -0.22 SPS 0.31 0.21 0.33 -0.07 SPS 0.3 0.26 0.17 0.38 NGPS 0.26 0.31 0.34 -0.19 NGPS 0.28 0.2 0.1 0.36 TGW 0.31 0.2 0.32 -0.07 TGW 0.29 0.26 0.19 0.39 GYP 0.2 0.07 0.46 0.44 GYP 0.29 -0.39 -0.27 0.11 BYP 0.3 -0.33 -0.08 0.12 BYP 0.25 -0.4 0.11 -0.15 HI -0.12 0.19 -0.09 0.85 HI 0.11 -0.32 -0.66 0.45 Under the drought condition, the first PC observed that it was highly related with PH (0.32), FLA (0.31), SL (0.30) and SPS (0.30) while it was negatively related with the DTH (-0.24) and DTM (-0.24). The second PC showed positive relation with SPS (0.26), TGW (0.26) and NGPS (0.20) and negatively related with SL (-0.03), FLA (-0.05), days to heading and maturity (-0.41) and PL ( -0.15) as mentioned in Table 4 . The third PC positively related with all studied traits like days to heading and maturity (0.39), PL (0.18) and FLA (0.24) while negatively related with HI (-0.66), GYP (-0.27) and SL (-0.06). The result of forth PC indicated that it was highly related with 1000-GW (0.39), NGPS (0.36) and days to heading and maturity (0.29) and it was negatively related with PH (-0.07), PL (-0.04), BYP (-0.15) and FLA (0.13) as displayed in Table 4 . Under the non-stressed environment, the projection of attributes on PC1 and PC2 showed the PH, FLA, peduncle length, SL, BYP and TP were associated positively with other while they exhibited negative correlation with other attributes as mentioned in the Fig. 3 . The attributes like NGPS, SPS, 1000-GW and GYP had positive association with each other and exhibited negative relation with other studied traits. The traits like days to heading and maturity expressed positive relation while had negative relation with other traits. The harvest index observed negative association with all the studied attributes as represented in Fig. 3 . Under the stressed condition, the projection of traits on PC1 and PC2 under drought condition revealed that thousand grain weight, spikelet per spike and number of grains per spike had positive association among them and showed negative association with all other attributes as mentioned in the Fig. 4. The traits like SL, FLA, PH, PL, TP, GYP, BYP and HI exhibited the positive association with each other. The only trait days to maturity had negative association with all other traits in the stressed condition as showed in Fig. 4. The results of this study showed that breeders might choose the best cultivars with the maximum number of grains/spike and grain yield in order to genetically increase spike yield. One of the finest breeding techniques for improving the genetics of seed production in bread wheat is the selection of the genotypes with the highest levels of seed yield and its constituent parts. Numerous studies have emphasized these findings(Leilah and Al-Khateeb 2005). In the normal condition, the genotypes G12, G33, G45 were opposite to the genotypes G 48, G35, G47, G42 and G 37. The genotypes G20, G23, G8, G6 and G10 were opposite to the G39, G43, G41 and G29. The genotype G12 showed the diversity from other genotypes as mentioned in Fig. 5 . The genotypes G20, G23, G8 had clear diversity from genotypes G48, G35, G 47, G42 and G37. The genotypes that were close to each other considered as drought tolerant while other genotypes considered as drought susceptible genotypes as displayed in Fig. 5 . In the drought environment, the genotypes G23, G12, G45 were opposite to the genotypes G48, G42, G47, G37 and G35. The genotypes G43 and G39 showed opposition to the G20, G8 and G 10 as displayed in Fig. 6 . The genotypes G48, G42, G47, G37 and G35 had clear diversity from other studied genotypes. The Fig. 6 observed clear difference between the drought susceptible and drought tolerant genotypes. The parameters grain/plant, biological yield, number of seeds/spike, and 1000-seed weight were proposed by (Arain, et al. 2011) in order to genetically increase spike yield selection under low water stress conditions.Breeders could be better able to achieve the intended improvement in bread wheat genotypes' tolerance to low water stress by increasing seed output under both drought and non-drought stress conditions. Conclusion In our research, we employed quantative characteristics to assess the correlation related to drought tolerance in 50 wheat genotypes. The experiment followed a randomized complete block design (RCBD) with three replications, wherein the wheat germplasm was evaluated under both normal and stressed environment. The analysis of variance (ANOVA) under normal and stressed conditions of the given traits showed highly significant differences which indicated the variation in the studied germplasm. The Genotypes that performed very well in both the normal and water deficit conditions are G12 followed by G8, G23, G20 and G16. These genotypes performed better in the drought conditions were screened as drought tolerant genotypes because they have highest mean values. The genotypes that performed poorly in both stresses and non-stressed were G35 followed by, G37, G47, G48 and G42. By using principal component analysis (PCA), the variation were observed in studied 50 wheat genotypes that were based on matrix of correlation to explain the variability and the relationship of wheat attributes under irrigated and drought conditions. In this experiment, our main objective is that to determine the association among 13 yield and yield related traits and focusing on the achievements of 50 wheat genotypes under non-stressed and stressed conditions. The information derived from this study will be beneficial for the new researchers and farmers to grow these genotypes for better yield and also would be used in breeding procedures to develop drought tolerant wheat genotypes. Declarations Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Author Contribution Authors Contribution Conceptualization, ANS; HGMDA methodology, MN; software, HGMDA validation, and AS; formal analysis, AU; investigation, GSH; resources, AAH; data curation, HGMDA, ANS, MAAD, and HMA; SFL; ANS; writing—original draft preparation, SE; writing—review and editing, SE; visualization, HGMDA All authors have read and agreed to the published version of the manuscript. Acknowledgments The authors extend their appreciation to the Researchers supporting project number (RSP2024R316) King Saud University, Riyadh, Saudi Arabia. Funding This work was funded by the the Researchers supporting project number (RSP2024R316) King Saud University, Riyadh, Saudi Arabia. References Ahamed, H., Khan, A. S., Kashif, M., & Khan, S. H. (2018). Genetic analysis of yield and physical traits of spring wheat grain. Journal of Genetic Engineering and Biotechnology, 16(2), 527-534. https://doi.org/10.1016/j.jgeb.2018.04.005 Ajmal, S. U., Minhas, N. M., Hamdani, A., Shakir, A., Zubair, M., & Ahmad, Z. (2013). Multivariate analysis of genetic divergence in wheat (Triticum aestivum) germplasm. Pakistan Journal of Botany, 45(5), 1643-1648. Amiri, R., Bahraminejad, S., & Jalali-Honarmand, S. (2013). Effect of terminal drought stress on grain yield and some morphological traits in 80 bread wheat genotypes. International Journal of Agriculture and Crop Sciences, 5(12), 1145-1150. Baloch, A. W., Baloch, M., & Ahmed, I. (2021). Association and path analysis in advance Pakistani bread wheat genotypes. Pure and Applied Biology (PAB), 10(1), 115-120. https://doi.org/10.19045/bspab.2021.100013 Barutcular, C., Yildirim, M., Koc, M., Dizlek, H., Akinci, C., El Sabagh, A., Saneoka, H., Ueda, A., Islam, M., & Toptas, I. (2016). Quality traits performance of bread wheat genotypes under drought and heat stress conditions. Fresenius Environmental Bulletin, 25(9), 6159-6165. Cossani, C. M., & Reynolds, M. P. (2012). Physiological traits for improving heat tolerance in wheat. Plant Physiology, 160(4), 1710-1718. https://doi.org/10.1104/pp.112.205062 Dhanda, S. S., Sethi, G. S., & Behl, R. K. (2004). Indices of drought tolerance in wheat genotypes at early stages of plant growth. Journal of Agronomy and Crop Science, 190(1), 6-12. https://doi.org/10.1111/j.1439-037X.2004.00047.x Ding, J., Huang, Z., Zhu, M., Li, C., Zhu, X., & Guo, W. (2018). Does cyclic water stress damage wheat yield more than a single stress? PLOS ONE, 13(4), e0195535. https://doi.org/10.1371/journal.pone.0195535 Ergen, N. Z., & Budak, H. (2009). Sequencing over 13 000 expressed sequence tags from six subtractive cDNA libraries of wild and modern wheats following slow drought stress. Plant, Cell & Environment, 32(3), 220-236. https://doi.org/10.1111/j.1365-3040.2008.01916.x Farooq, M., Bramley, H., Palta, J. A., & Siddique, K. H. M. (2011). Heat stress in wheat during reproductive and grain-filling phases. Critical Reviews in Plant Sciences, 30(6), 491-507. https://doi.org/10.1080/07352689.2011.615687 Fleury, D., Jefferies, S., Kuchel, H., & Langridge, P. (2010). Genetic and genomic tools to improve drought tolerance in wheat. Journal of Experimental Botany, 61(12), 3211-3222. https://doi.org/10.1093/jxb/erq152 Gupta, P. K., Balyan, H. S., & Gahlaut, V. (2017). QTL analysis for drought tolerance in wheat: Present status and future possibilities. Agronomy, 7(1), 5. https://doi.org/10.3390/agronomy7010005 Hays, D. B., Do, J. H., Mason, R. E., Morgan, G., & Finlayson, S. A. (2007). Heat stress induced ethylene production in developing wheat grains induces kernel abortion and increased maturation in a susceptible cultivar. Plant Science, 172(6), 1113-1123. https://doi.org/10.1016/j.plantsci.2007.03.004 Kaya, Y., Palta, C., & Taner, S. (2002). Additive main effects and multiplicative interactions analysis of yield performances in bread wheat genotypes across environments. Theoretical and Applied Genetics, 105(2-3), 847-854. https://doi.org/10.1007/s00122-002-0958-1 Mondal, S., Singh, R. P., Crossa, J., Huerta-Espino, J., Sharma, I., Chatrath, R., Singh, G. P., Sohu, V. S., Mavi, G. S., Sukaru, V. S. P., Kalappanavar, I. K., Mishra, V. K., Hussain, M., Gautam, N. R., Uddin, J., Barma, N. C. D., Hakim, M. A., & Joshi, A. K. (2016). Earliness in wheat: A key to adaptation under terminal and continual high temperature stress in South Asia. Field Crops Research, 192, 19-26. https://doi.org/10.1016/j.fcr.2016.04.015 Nezhad, K. Z., Weber, W. E., & Röder, M. S. (2012). Molecular and physiological responses to abiotic stress in forest trees and their relevance to tree breeding. Tree Genetics & Genomes, 8(4), 725-746. https://doi.org/10.1007/s11295-012-0491-5 Ozturk, A., Aydin, F., & Karaman, M. (2002). Yield response of wheat and barley to water stress under Konya conditions. Pakistan Journal of Botany, 34(4), 869-877. Pinto, R. S., Reynolds, M. P., Mathews, K. L., McIntyre, C. L., Olivares-Villegas, J. J., & Chapman, S. C. (2010). Heat and drought adaptive QTL in a wheat population designed to minimize confounding agronomic effects. Theoretical and Applied Genetics, 121(6), 1001-1021. https://doi.org/10.1007/s00122-010-1351-4 Trethowan, R. M., & Mujeeb-Kazi, A. (2008). Novel germplasm resources for improving environmental stress tolerance of hexaploid wheat. Crop Science, 48(4), 1255-1265. https://doi.org/10.2135/cropsci2007.09.0512 Zahra, S., Maqbool, K., Irfan, M., & Jamil, M. (2021). An overview of effects of climate change on selected crops of Pakistan: Adaptive strategies. Journal of Environmental Management, 277, 111431. https://doi.org/10.1016/j.jenvman.2020.111431 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4001110","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":276461709,"identity":"03ce8dd9-6d01-4c83-b577-acfb26e28e81","order_by":0,"name":"Adnan Noor Shah","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDUlEQVRIiWNgGAWjYFCCBCgpwXyYoYKBgQfEkwBiAyK0sCUznAFq4SFBC48xSAsDQS387bkHPxcw1OXxz+75bHCwzUbGnoH54G0ehjvGuLRInHmXLD2D4XCxxJ2zmxMOtqUBHcaWbM3D8MwMp7tu5BhI8zAcSGy4kbv58Me2w0AtPGZAkcM2uHTI38gx/s3DUJc4/0bO4wMH2/4DtfB/w6vF4EYOyEzmxA03cpiBDjsAsoUNpAWnwwzPvDGz5jE4nLjxRpqxwYFzyTw8h9mMLecYPMPpfbnjOca3eSrqEufdSH4scaDMzp69vfnhjTcVdwwbcPof7DxkDjNY5ABeDVgBGVpGwSgYBaNguAIAjidUxQSDHdQAAAAASUVORK5CYII=","orcid":"","institution":"Khwaja Fareed University of Engineering and Information Technology","correspondingAuthor":true,"prefix":"","firstName":"Adnan","middleName":"Noor","lastName":"Shah","suffix":""},{"id":276461710,"identity":"6ee5bc73-c8d8-42bf-9dfa-d52c612a55b3","order_by":1,"name":"Hafiz Ghulam","email":"","orcid":"","institution":"The Islamia University of Bahawalpur","correspondingAuthor":false,"prefix":"","firstName":"Hafiz","middleName":"","lastName":"Ghulam","suffix":""},{"id":276461711,"identity":"3464b11e-7d94-4bfb-8212-73dffc1df332","order_by":2,"name":"Muhammad Naeem","email":"","orcid":"","institution":"The Islamia University of Bahawalpur","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"","lastName":"Naeem","suffix":""},{"id":276461712,"identity":"fe53639a-1827-4479-bfb5-f1da1b6023d8","order_by":3,"name":"Aziz Ullah","email":"","orcid":"","institution":"University of Sargodha","correspondingAuthor":false,"prefix":"","firstName":"Aziz","middleName":"","lastName":"Ullah","suffix":""},{"id":276461713,"identity":"42216600-f0a8-40e1-9f6c-f1cf822e301d","order_by":4,"name":"Amjad Saeed","email":"","orcid":"","institution":"The Islamia University of Bahawalpur","correspondingAuthor":false,"prefix":"","firstName":"Amjad","middleName":"","lastName":"Saeed","suffix":""},{"id":276461714,"identity":"5bd1ff8f-7451-443c-86fd-2263bb81729f","order_by":5,"name":"Ghulam Sabir Hussain","email":"","orcid":"","institution":"Fatima Fertilizer Company","correspondingAuthor":false,"prefix":"","firstName":"Ghulam","middleName":"Sabir","lastName":"Hussain","suffix":""},{"id":276461715,"identity":"df149b73-216d-43cd-a5dc-a48b21132ee3","order_by":6,"name":"Ashraf Atef Hatamleh","email":"","orcid":"","institution":"King Saud University","correspondingAuthor":false,"prefix":"","firstName":"Ashraf","middleName":"Atef","lastName":"Hatamleh","suffix":""},{"id":276461716,"identity":"1117f6ad-7df8-411e-bd69-865bf25db660","order_by":7,"name":"Munirah Abdullah Al-Dosary","email":"","orcid":"","institution":"King Saud University","correspondingAuthor":false,"prefix":"","firstName":"Munirah","middleName":"Abdullah","lastName":"Al-Dosary","suffix":""},{"id":276461717,"identity":"781d8e58-d46f-4c2c-8331-db0a8c4afa89","order_by":8,"name":"Sobhi F. Lamlom","email":"","orcid":"","institution":"Alexandria University","correspondingAuthor":false,"prefix":"","firstName":"Sobhi","middleName":"F.","lastName":"Lamlom","suffix":""},{"id":276461718,"identity":"30186297-8944-4809-947a-0aae0e141d0b","order_by":9,"name":"Sezai Ercisli","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Sezai","middleName":"","lastName":"Ercisli","suffix":""}],"badges":[],"createdAt":"2024-02-29 20:31:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4001110/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4001110/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52169235,"identity":"e58349cf-2dbc-472e-bffa-d36049fff9c3","added_by":"auto","created_at":"2024-03-07 15:56:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":209481,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation cluster map of 13 yield and yield related traits under normal conditions\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4001110/v1/00b67ab24618f9e3d7599280.png"},{"id":52169234,"identity":"1627a87a-1c5f-495c-8f67-8828bc1beda6","added_by":"auto","created_at":"2024-03-07 15:56:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":202643,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation cluster map of 13 yield and yield related traits under drought conditions\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4001110/v1/5183f0e9024f0e5290397ea9.png"},{"id":52169231,"identity":"05198091-1f71-4969-a5d7-df413c78be28","added_by":"auto","created_at":"2024-03-07 15:56:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":17175,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePCA of Loading Plot for13 yield and yield related traits under normal condition\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4001110/v1/59e53ac59ca9783fdac6948d.png"},{"id":52169236,"identity":"ac7b0a32-fa6d-416f-bc9c-1375b49f6aa7","added_by":"auto","created_at":"2024-03-07 15:56:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":16469,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePCA of Loading Plot for 13 yield and yield related traits under drought condition\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4001110/v1/02c06a024e576291f6523e7e.png"},{"id":52170371,"identity":"5e199614-3cab-4048-8ebc-b5a59ea9f4b1","added_by":"auto","created_at":"2024-03-07 16:04:31","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":25443,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePCA of score plot for 50 wheat genotypes under normal conditions\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4001110/v1/10cc0448f79c1f38d9a82873.png"},{"id":52169232,"identity":"8a68c6b2-af7c-4900-ab71-21cddec8478f","added_by":"auto","created_at":"2024-03-07 15:56:31","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":25072,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePCA of score plot for 50 wheat genotypes under drought conditions\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4001110/v1/dc85490bbc2c4107f59b9d9a.png"},{"id":53789695,"identity":"addbcb05-04d9-4830-8eef-c1f988cd3e96","added_by":"auto","created_at":"2024-03-30 16:37:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":824950,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4001110/v1/45064898-7725-4320-9263-0c2d1239d091.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessment of Drought Tolerance in Wheat genotypes for Sustainable Food Security and Breeding Programs","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe most important cultivated crop wheat (\u003cem\u003eTriticumaestivum\u003c/em\u003e) belongs to the cereal grass family \u003cem\u003ePoaceae\u003c/em\u003e. Genus \u003cem\u003eTriticum\u003c/em\u003e is comprised of many species out of which two major species are \u003cem\u003eTriticumaestivum\u003c/em\u003e and \u003cem\u003eTriticum durum\u003c/em\u003e. Globally wheat is consumed as a staple food which is majorly cultivated around most of the earth\u0026rsquo;s areas and requires consideration because of its significance for long-term food security. Around 30% of the world\u0026rsquo;s surface is covered with wheat (Pocketbook 2015) and it ranks at 2nd position in terms of production after maize crop (Cossani and Reynolds \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). A minimal decrease in the cultivated area as compared to the last year in Pakistan, the production of the wheat crop is set to reach about 27\u0026nbsp;million tons in the year 2023-24, which will be 2% higher than the last growing season (Panneer et al. 2023). Yet, demand for wheat is increasing as well and is expected to keep doing so at an annual average of approximately about 2% (Jaradat 2011).\u003c/p\u003e \u003cp\u003eThe occurrence and severity of drought are predicted to increase in coming years as a result of the current climate change which will have significant impact on the patterns of precipitation and temperature (Ergen and Budak \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Most significant abiotic stress factor which is causing agricultural yield reductions is drought, hence high-yielding crops are important even in ecologically stressed situations (Fleury et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The situation is not new as the demand is increasing annually due to increase in the population pressure, similar situation was also faced by the world which was mitigated by genetically modified high grain yielding wheat crop 50 years ago which coined as Green Revolution (Tester and Langridge 2010).\u003c/p\u003e \u003cp\u003eTolerance for the drought is intricate with environmental interactions. The evaluation of response in drought environment by plant, its mode and time of response is directly related to the extremity of the cell dehydration during the stress period and the significance of its interaction with both abiotic and biotic stress factors (Ribaut 2006). The breeding for the drought resistant varieties is a complicated process as many other environmental factors are also involved and compete with the crop plants altogether. The factors like extreme temperature, light intensity, shortage of water and deficiency of nutrients generally effects the normal growing condition but cannot be manageable through conventional farm procedures. Some soil may exhibit certain properties which can also affect the balance of different environmental factors which can be composition and structure (Whitmore and Whalley 2009). The rate by which the yield is increasing is still low to meet the demands of wheat 2050 (Mwadzingeni et al. 2016). The duration and impact of the water scarcity can determine the loss of yield by decreasing the life cycle and time needed for proper grain filling (Farooq et al. 2014). The rate of transpiration is significantly decreased when plants face water shortage, due to closure of stomata the heat is trapped and leaves temperature is increased, it leads to the disruption of the physiological processes happening in the plant (Manunta et al. 2002).\u003c/p\u003e \u003cp\u003eThe attributes that influence the grain yield of the wheat cultivars are number of panicles, grain weight and biological yield (Leilah and Al-Khateeb 2005). Considering the limitations posed by climate change and water scarcity, it will be important to improve biomass production and economic yield (Spiertz 2013).\u003c/p\u003e \u003cp\u003eThe major aim for the current study is to improve and develop wheat cultivars under the drought stress and to increase the yield potential of the wheat. The 50 studied genotypes were selected to evaluate with Pearson\u0026rsquo;s correlation and PCA (principal component analysis) under the stressed and non-stressed conditions.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eThe present study was took place in the arid region of the Punjab, specifically at the Department of Plant Breeding and Genetics, The Islamia University of Bahawalpur. Randomized Complete Block design (RCBD) was applied to 50 wheat genotypes and were evaluated in three replications under non-stressed (normal) irrigated and stressed (less water) drought condition during the cropping year 2022\u0026ndash;2023. 3 seeds were sown and later thinning was done to evaluate a single plant. The row-to-row distance was 12 inches. The normal field was irrigated 5 times until the maturity while the drought stresses plot was watered only once at the flowering stage (Anthesis) and no rainfall was reported during growing season. Rate at which the fertilizer was applied was 150:100:50 kilograms N: P: K per hectare which was enough to achieve maximum crop potential. The attributes which were recorded were Plant Height (PH), Flag Leaf Area (FLA), Tillers per plant (TP), Peduncle length (PL), Days to heading (DH), Days to maturity (DM), Spike Length (SL), Spikelet per Spike (SS), Number of grains per spike (NGS), 1000-grain weight (TGW), Grain yield per plant (GYP), Biological yield per plant (BYP) Harvest index (HI).\u003c/p\u003e \u003cp\u003eAnalysis of variance and Pearson\u0026rsquo;s correlation was calculated from the collected data using the software statistix 8.1. The correlation cluster map was developed using R-studio and Principle component analysis using XLstat.\u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003cp\u003eThe ANOVA (Analysis Of Variance) presented in the Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e which indicated significant difference between the treatments and genotypes. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e also represented the significant interaction among the studied genotypes and the treatment. The Genotypes that performed very well in both the normal and water deficit conditions are G12 followed by G8, G23, G20 and G16 and indicated as genotypes with drought tolerance while the cultivars that performed poorly under both conditions were G35 followed by, G37, G47, G48 and G42 considered as drought susceptible genotypes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of variance for studied attributes under normal and drought conditions of 50 wheat genotypes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReplication\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGenotypes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGenotypes*Treatment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eError\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e299\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.98**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6709.79**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.5**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFLA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.409**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e864.13**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.62**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTPP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.118**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7834**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1653**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.355**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96.141**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4403**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDTH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.43**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e875.86**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.098**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDTM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.770**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2122.57**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.6351**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.7**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20003.4**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78.4**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.52**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1012.37**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.77**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNGS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.62**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3310.71**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.95**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTGW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.425**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e240.263**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.473**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGYP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.343**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94.7532**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4332**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBYP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.02418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73.35**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29024.3**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53.7684**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.26**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e248.084**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.218**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSov (source of variance), DF (degree of freedom), ** (highly significant), Plant Height (PH), Flag Leaf Area (FLA), Tillers per plant (TP), Peduncle length (PL), Days to heading (DH), Days to maturity (DM), Spike Length (SL), Spikelet per Spike (SS), Number of grains per spike (NGS), 1000-grain weight (TGW), Grain yield per plant (GYP), Biological yield per plant (BYP) Harvest index (HI)\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003ePearson\u0026rsquo;s Correlation Analysis for the studied attribute\u003c/h2\u003e \u003cp\u003eIn the both conditions, the flag leaf area and plant height displayed highly significant and positive relationship with FLA, TP, PL, BYP, SL, SPS, NGS, 1000-GW and GYP as mentioned in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and represented in Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u0026amp; \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Different scientists also reported the same results. Lack of availability of water resulted in a decline in photosynthesis and uptake of other essential nutrients that subsequently reduces plant height (Sarto et al. 2017). Similar findings on reduction of the trait plant height under drought stressed environment were reported (Naghavi et al. 2015). Under the normal environmental condition significant and positive association of flag leaf length with plant height and peduncle length was reported(Amiri et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). A recent study concluded more the area of the flag leaf increase the levels of photosynthesis in the plant. Flag leaf area is significantly associated with tolerance for drought and is an important component of yield (Yani and Rashidi 2012).\u003c/p\u003e \u003cp\u003eIn both normal non-stressed environment and stressed drought conditions, the tillers per plant indicated that there was a strong and positive association that is statistically significant with PL, BYP, SL, SPS, NGS, 1000-GW and GYP as showed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u0026amp; \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Under the both normal and drought condition, tillers per plant showed positive association with all the attributes (Zahra et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Recovery duration decrease the number of tillers under water stress condition and overall result revealed lower number of tillers (Ding et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe peduncle length showed positive and highly significant relationship with the biological yield per plant, spike length, spikelet per spike, number of grains per spike under both mentioned environments and thousand grain weight had highly significant association only under normal condition. The peduncle length showed significant and positive correlation with grain yield per plant as mentioned in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and expressed in Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u0026amp; \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Similar results under the normal (non-stressed) condition were confirmed by other researcher (Khan et al. 2010).\u003c/p\u003e \u003cp\u003eUnder the non-stressed and stressed conditions, the biological yield per plant associated positively and highly significant with studied attributes like spike length, spikelet per spike, number of grains per spike, and grain yield per plant. as displayed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and also expressed in Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u0026amp; \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Biological yield per plant (BYP) observed positive and significant association with 1000-grain weight under drought stress while it showed highly significant association under normal condition Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u0026amp; 4. These traits were positively related and have the significant correlation between them. These results were matched with research report (Peymaninia et al. 2012).\u003c/p\u003e \u003cp\u003eDays to maturity and days to heading correlated highly significant and positively with each other while these attributes displayed negative but high significant correlation with other attributes like spike length, spikelet per spike, number of grains per spike and thousand grain weight had negative and highly significant association with days to heading. As displayed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and in Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u0026amp; \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The days to heading had negative relation with all studied traits except day to maturity which was also confirmed by the researcher (Barutcular et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). These results also matched with the research of (Zafarnaderi et al. 2013).\u003c/p\u003e \u003cp\u003eThe spike length exhibited the correlation that was highly significant and positive with all the studied attributes like SPS, NGS, 1000GW and GYP under irrigated and non-irrigated conditions. The harvest index that mentioned significant association with spike length under normal irrigated condition and non-significant correlation under drought condition as showed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and in Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u0026amp; \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Spike length had the positive association with the biological yield that was also found by (Peymaninia et al. 2012). Similar finding followed by (Slafer and Andrade 1993) that under water deficit condition spike length and grain yield correlated significantly.\u003c/p\u003e \u003cp\u003eUnder the non-stressed and stressed conditions, the spikelet per spike mentioned positively highly significant relation with the number of grains per spike, 1000-grain weight and grain yield per plant as showed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;4. Similar results explained by (Baloch et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Positive and significant result showed by spikelet per spike with the spike length (Ajmal et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe number of grains per spike associated positively and highly significant with 1000-GW and GYP. The spikelet per spike also exhibited negatively non-significant association with harvest index as expressed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and in Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u0026amp; \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e under the normal and stressed conditions.\u003c/p\u003e \u003cp\u003eUnder the normal irrigated condition, the thousand grain weight exhibited the highly significant and positively associated with harvest index and grain yield per plant had negative and significant relationship with it as mentioned in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Under the water deficit condition, the highly significant and positive association showed by the grain yield per plant and HI had positive and non-significant association with 1000-grain weight as represented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and in Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u0026amp; \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. These results collaborated with the results of research by the researcher (Ahamed et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These findings also matched with the result of research by the researcher (Mollasadeghi et al. 2011). He observed in his experiment that 1000-grain weight had positive association with grain yield.\u003c/p\u003e \u003cp\u003eThe grain yield per plant mentioned the non-significant and positive relation with harvest index under the non-stressedl condition as showed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u0026amp; \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e while GYP correlated hghly significant and positively with harvest index (0.69**) under the stressed condition. Grain yield per spike was the positively and significant associated with the grain yield per plant. Both traits were positively connected. These observations were agreed with the result of researcher (Nasri et al. 2014). The similar findings also matched with the results of the research by the researcher (Abderrahmane et al. 2013).\u003c/p\u003e \u003cp\u003eThe traits like (FLA), (TP), (PH), (PL), (SL), (BYP), (SPS), (NGPS) and 1000-GW had negative association with the HI under irrigated condition while under low water condition harvest index had positive but non-significant association with studied traits as displayed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u0026amp; \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation studies among 13 yield related traits under stressed and non-stressed conditions in 50 wheat genotypes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"14\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConditions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFLA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTPP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBYP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSPS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNGPS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eTGW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eGYP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFLA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.79**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFLA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDrought\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.81**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTPP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.84**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.78**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTPP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDrought\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.63**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.71**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.84**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.77**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.87**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDrought\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.87**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.79**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBYP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.76**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.87**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.84**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBYP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDrought\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.65**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.55**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.54**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.77**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDrought\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDrought\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.74**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.83**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.88**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.91**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDrought\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.71**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.62**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.56**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.76**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.64**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.72**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.66**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.54**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.66**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.58**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.7**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDrought\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.64**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.63**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.7**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.35**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.56**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNGPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.56**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.36**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.47**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.43**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.56**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.85**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNGPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDrought\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.59**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.57**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.41**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.66**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.53**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.82**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTGW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.71**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.68**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.54**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.64**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.58**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.7**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.98**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.82**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTGW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDrought\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.63**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.63**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.39**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.67**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.34*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.54**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.98**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.8**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGYP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.56**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.42**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.34*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.34*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.43**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.49**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.59**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.42**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.6**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGYP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDrought\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.76**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.62**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.65**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.74**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.69**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.63**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.46**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.49**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.44**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.09ns\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.1ns\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.01ns\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDrought\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2ns\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.08ns\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.13ns\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.19ns\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.23ns\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.18ns\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.17ns\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.21ns\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.15ns\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.69**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePlant Height (PH), Flag Leaf Area (FLA), Tillers per plant (TP), Peduncle length (PL), Days to heading (DH), Days to maturity (DM), Spike Length (SL), Spikelet per Spike (SS), Number of grains per spike (NGS), 1000-grain weight (TGW), Grain yield per plant (GYP), Biological yield per plant (BYP) Harvest index (HI)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003ePRINCIPAL COMPONENT ANALYSIS\u003c/h2\u003e \u003cp\u003eIn order to explain the variability and relationship of wheat attributes under normal and drought environments, the variations were mentioned in studied 50 wheat genotypes that based on correlation by using principal component analysis (PCA).\u003c/p\u003e \u003cp\u003eIn the studied genotypes, only 4 attributes showed the significant value under the both non-stressed and stressed conditions as mentioned in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The trait that exhibited eigenvalue more than one considered as significant. The eigenvalue that showed value less than one considered as non-significant. The 4 traits observed significant eigenvalue in the normal and drought environments, while other traits did not exhibit significant values. The statistically significant principal components were selected based on significant eigenvalues which was mentioned by (Kaiser 1960) The PCs that had eigenvalues higher than one were considered as significant.\u003c/p\u003e \u003cp\u003eUnder the normal condition, the PC1 indicated the 78.7% for the variance, PC2 indicated 14.6%, PC3 indicated 12.2% and PC4 indicated 10.1%. In the drought condition, the PC1 assumed for 74.5%, PC2 assumed for 16.7%, PC3 assumed for 11.6% and PC4 assumed for 11.1% as showed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e In breeding procedures for selection of parents biplot analysis was used previously (Sisodia and Rai 2017). In order to chose various parents for breeding procedures and hybridization, principal component analysis is very useful (Khodadadi et al. 2011).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEigen analysis of 13 yield and yield related indices under irrigated and stressed conditions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEnv.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eEigenvalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eProportion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eCumulative\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDrought\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDrought\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDrought\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePC3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePC4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePC5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePC6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePC7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePC8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePC9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePC10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePC11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePC12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePC13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eUnder the normal condition, the result of first PC showed that it related highly with PH (0.32), PL (0.32) and SL (0.32) while it was negatively related with the DTH (-0.24), DTM (-0.24) and HI (-0.12). The second PC indicated that it highly related with the number of grains per spike (0.31) and spikelets per spike (0.21) but it showed negative relation with all studied traits like PH (-0.12), FLA (-0.06), PL (-0.25) tillers per plant (-0.38) and biological yield per plant (-0.33) as mentioned in Table # 4. The third PC observed that it was highly related with grain yield per plant (0.46),DTH (0.45), DTM (0.42) and number of grains per spike (0.34) and it was negatively related with PH (-0.03), biological yield per plant (-0.08), HI (-0.09), FLA (-0.14) and tillers per plant (-0.17). The result of forth PC presented that it was positively related with HI (0.85), GYP (0.44), TP (0.12) and BYP (0.12) while it was negatively related with DTH (-0.01), PL (-0.04), TGW (-0.07) and NGS (-0.19) as showed in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The shortage of water causes loss of water from plant tissues, which causes the disruption of both membrane structure and function. The first organ to be damaged due to the water shortage in plant is plant cell membrane, the plant potential against drought determines the sustainability and integrity of plant under drought conditions. This is the basic criteria to be used for the discrimination against tolerant and susceptible wheat varieties (Dhanda et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePCA Variability of 13 yield and yield related traits under normal and low water conditions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePC1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePC2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePC3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePC4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFLA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFLA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTPP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTPP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNGPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNGPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTGW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTGW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGYP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGYP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBYP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBYP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eUnder the drought condition, the first PC observed that it was highly related with PH (0.32), FLA (0.31), SL (0.30) and SPS (0.30) while it was negatively related with the DTH (-0.24) and DTM (-0.24). The second PC showed positive relation with SPS (0.26), TGW (0.26) and NGPS (0.20) and negatively related with SL (-0.03), FLA (-0.05), days to heading and maturity (-0.41) and PL ( -0.15) as mentioned in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The third PC positively related with all studied traits like days to heading and maturity (0.39), PL (0.18) and FLA (0.24) while negatively related with HI (-0.66), GYP (-0.27) and SL (-0.06). The result of forth PC indicated that it was highly related with 1000-GW (0.39), NGPS (0.36) and days to heading and maturity (0.29) and it was negatively related with PH (-0.07), PL (-0.04), BYP (-0.15) and FLA (0.13) as displayed in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eUnder the non-stressed environment, the projection of attributes on PC1 and PC2 showed the PH, FLA, peduncle length, SL, BYP and TP were associated positively with other while they exhibited negative correlation with other attributes as mentioned in the Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The attributes like NGPS, SPS, 1000-GW and GYP had positive association with each other and exhibited negative relation with other studied traits. The traits like days to heading and maturity expressed positive relation while had negative relation with other traits. The harvest index observed negative association with all the studied attributes as represented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Under the stressed condition, the projection of traits on PC1 and PC2 under drought condition revealed that thousand grain weight, spikelet per spike and number of grains per spike had positive association among them and showed negative association with all other attributes as mentioned in the Fig.\u0026nbsp;4. The traits like SL, FLA, PH, PL, TP, GYP, BYP and HI exhibited the positive association with each other. The only trait days to maturity had negative association with all other traits in the stressed condition as showed in Fig.\u0026nbsp;4. The results of this study showed that breeders might choose the best cultivars with the maximum number of grains/spike and grain yield in order to genetically increase spike yield. One of the finest breeding techniques for improving the genetics of seed production in bread wheat is the selection of the genotypes with the highest levels of seed yield and its constituent parts. Numerous studies have emphasized these findings(Leilah and Al-Khateeb 2005).\u003c/p\u003e\u003cp\u003eIn the normal condition, the genotypes G12, G33, G45 were opposite to the genotypes G 48, G35, G47, G42 and G 37. The genotypes G20, G23, G8, G6 and G10 were opposite to the G39, G43, G41 and G29. The genotype G12 showed the diversity from other genotypes as mentioned in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The genotypes G20, G23, G8 had clear diversity from genotypes G48, G35, G 47, G42 and G37. The genotypes that were close to each other considered as drought tolerant while other genotypes considered as drought susceptible genotypes as displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e. In the drought environment, the genotypes G23, G12, G45 were opposite to the genotypes G48, G42, G47, G37 and G35. The genotypes G43 and G39 showed opposition to the G20, G8 and G 10 as displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e. The genotypes G48, G42, G47, G37 and G35 had clear diversity from other studied genotypes. The Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e observed clear difference between the drought susceptible and drought tolerant genotypes. The parameters grain/plant, biological yield, number of seeds/spike, and 1000-seed weight were proposed by (Arain, et al. 2011) in order to genetically increase spike yield selection under low water stress conditions.Breeders could be better able to achieve the intended improvement in bread wheat genotypes' tolerance to low water stress by increasing seed output under both drought and non-drought stress conditions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn our research, we employed quantative characteristics to assess the correlation related to drought tolerance in 50 wheat genotypes. The experiment followed a randomized complete block design (RCBD) with three replications, wherein the wheat germplasm was evaluated under both normal and stressed environment. The analysis of variance (ANOVA) under normal and stressed conditions of the given traits showed highly significant differences which indicated the variation in the studied germplasm. The Genotypes that performed very well in both the normal and water deficit conditions are G12 followed by G8, G23, G20 and G16. These genotypes performed better in the drought conditions were screened as drought tolerant genotypes because they have highest mean values. The genotypes that performed poorly in both stresses and non-stressed were G35 followed by, G37, G47, G48 and G42. By using principal component analysis (PCA), the variation were observed in studied 50 wheat genotypes that were based on matrix of correlation to explain the variability and the relationship of wheat attributes under irrigated and drought conditions. In this experiment, our main objective is that to determine the association among 13 yield and yield related traits and focusing on the achievements of 50 wheat genotypes under non-stressed and stressed conditions. The information derived from this study will be beneficial for the new researchers and farmers to grow these genotypes for better yield and also would be used in breeding procedures to develop drought tolerant wheat genotypes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eDeclaration of competing interest\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthors Contribution Conceptualization, ANS; HGMDA methodology, MN; software, HGMDA validation, and AS; formal analysis, AU; investigation, GSH; resources, AAH; data curation, HGMDA, ANS, MAAD, and HMA; SFL; ANS; writing\u0026mdash;original draft preparation, SE; writing\u0026mdash;review and editing, SE; visualization, HGMDA All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eThe authors extend their appreciation to the Researchers supporting project number (RSP2024R316) King Saud University, Riyadh, Saudi Arabia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by the the Researchers supporting project number (RSP2024R316) King Saud University, Riyadh, Saudi Arabia.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAhamed, H., Khan, A. S., Kashif, M., \u0026amp; Khan, S. H. (2018). Genetic analysis of yield and physical traits of spring wheat grain. Journal of Genetic Engineering and Biotechnology, 16(2), 527-534. https://doi.org/10.1016/j.jgeb.2018.04.005\u003c/li\u003e\n\u003cli\u003eAjmal, S. U., Minhas, N. M., Hamdani, A., Shakir, A., Zubair, M., \u0026amp; Ahmad, Z. (2013). Multivariate analysis of genetic divergence in wheat (Triticum aestivum) germplasm. Pakistan Journal of Botany, 45(5), 1643-1648.\u003c/li\u003e\n\u003cli\u003eAmiri, R., Bahraminejad, S., \u0026amp; Jalali-Honarmand, S. (2013). Effect of terminal drought stress on grain yield and some morphological traits in 80 bread wheat genotypes. International Journal of Agriculture and Crop Sciences, 5(12), 1145-1150.\u003c/li\u003e\n\u003cli\u003eBaloch, A. W., Baloch, M., \u0026amp; Ahmed, I. (2021). Association and path analysis in advance Pakistani bread wheat genotypes. Pure and Applied Biology (PAB), 10(1), 115-120. https://doi.org/10.19045/bspab.2021.100013\u003c/li\u003e\n\u003cli\u003eBarutcular, C., Yildirim, M., Koc, M., Dizlek, H., Akinci, C., El Sabagh, A., Saneoka, H., Ueda, A., Islam, M., \u0026amp; Toptas, I. (2016). Quality traits performance of bread wheat genotypes under drought and heat stress conditions. Fresenius Environmental Bulletin, 25(9), 6159-6165.\u003c/li\u003e\n\u003cli\u003eCossani, C. M., \u0026amp; Reynolds, M. P. (2012). Physiological traits for improving heat tolerance in wheat. Plant Physiology, 160(4), 1710-1718. https://doi.org/10.1104/pp.112.205062\u003c/li\u003e\n\u003cli\u003eDhanda, S. S., Sethi, G. S., \u0026amp; Behl, R. K. (2004). Indices of drought tolerance in wheat genotypes at early stages of plant growth. Journal of Agronomy and Crop Science, 190(1), 6-12. https://doi.org/10.1111/j.1439-037X.2004.00047.x\u003c/li\u003e\n\u003cli\u003eDing, J., Huang, Z., Zhu, M., Li, C., Zhu, X., \u0026amp; Guo, W. (2018). Does cyclic water stress damage wheat yield more than a single stress? PLOS ONE, 13(4), e0195535. https://doi.org/10.1371/journal.pone.0195535\u003c/li\u003e\n\u003cli\u003eErgen, N. Z., \u0026amp; Budak, H. (2009). Sequencing over 13 000 expressed sequence tags from six subtractive cDNA libraries of wild and modern wheats following slow drought stress. Plant, Cell \u0026amp; Environment, 32(3), 220-236. https://doi.org/10.1111/j.1365-3040.2008.01916.x\u003c/li\u003e\n\u003cli\u003eFarooq, M., Bramley, H., Palta, J. A., \u0026amp; Siddique, K. H. M. (2011). Heat stress in wheat during reproductive and grain-filling phases. Critical Reviews in Plant Sciences, 30(6), 491-507. https://doi.org/10.1080/07352689.2011.615687\u003c/li\u003e\n\u003cli\u003eFleury, D., Jefferies, S., Kuchel, H., \u0026amp; Langridge, P. (2010). Genetic and genomic tools to improve drought tolerance in wheat. Journal of Experimental Botany, 61(12), 3211-3222. https://doi.org/10.1093/jxb/erq152\u003c/li\u003e\n\u003cli\u003eGupta, P. K., Balyan, H. S., \u0026amp; Gahlaut, V. (2017). QTL analysis for drought tolerance in wheat: Present status and future possibilities. Agronomy, 7(1), 5. https://doi.org/10.3390/agronomy7010005\u003c/li\u003e\n\u003cli\u003eHays, D. B., Do, J. H., Mason, R. E., Morgan, G., \u0026amp; Finlayson, S. A. (2007). Heat stress induced ethylene production in developing wheat grains induces kernel abortion and increased maturation in a susceptible cultivar. Plant Science, 172(6), 1113-1123. https://doi.org/10.1016/j.plantsci.2007.03.004\u003c/li\u003e\n\u003cli\u003eKaya, Y., Palta, C., \u0026amp; Taner, S. (2002). Additive main effects and multiplicative interactions analysis of yield performances in bread wheat genotypes across environments. Theoretical and Applied Genetics, 105(2-3), 847-854. https://doi.org/10.1007/s00122-002-0958-1\u003c/li\u003e\n\u003cli\u003eMondal, S., Singh, R. P., Crossa, J., Huerta-Espino, J., Sharma, I., Chatrath, R., Singh, G. P., Sohu, V. S., Mavi, G. S., Sukaru, V. S. P., Kalappanavar, I. K., Mishra, V. K., Hussain, M., Gautam, N. R., Uddin, J., Barma, N. C. D., Hakim, M. A., \u0026amp; Joshi, A. K. (2016). Earliness in wheat: A key to adaptation under terminal and continual high temperature stress in South Asia. Field Crops Research, 192, 19-26. https://doi.org/10.1016/j.fcr.2016.04.015\u003c/li\u003e\n\u003cli\u003eNezhad, K. Z., Weber, W. E., \u0026amp; R\u0026ouml;der, M. S. (2012). Molecular and physiological responses to abiotic stress in forest trees and their relevance to tree breeding. Tree Genetics \u0026amp; Genomes, 8(4), 725-746. https://doi.org/10.1007/s11295-012-0491-5\u003c/li\u003e\n\u003cli\u003eOzturk, A., Aydin, F., \u0026amp; Karaman, M. (2002). Yield response of wheat and barley to water stress under Konya conditions. Pakistan Journal of Botany, 34(4), 869-877.\u003c/li\u003e\n\u003cli\u003ePinto, R. S., Reynolds, M. P., Mathews, K. L., McIntyre, C. L., Olivares-Villegas, J. J., \u0026amp; Chapman, S. C. (2010). Heat and drought adaptive QTL in a wheat population designed to minimize confounding agronomic effects. Theoretical and Applied Genetics, 121(6), 1001-1021. https://doi.org/10.1007/s00122-010-1351-4\u003c/li\u003e\n\u003cli\u003eTrethowan, R. M., \u0026amp; Mujeeb-Kazi, A. (2008). Novel germplasm resources for improving environmental stress tolerance of hexaploid wheat. Crop Science, 48(4), 1255-1265. https://doi.org/10.2135/cropsci2007.09.0512\u003c/li\u003e\n\u003cli\u003eZahra, S., Maqbool, K., Irfan, M., \u0026amp; Jamil, M. (2021). An overview of effects of climate change on selected crops of Pakistan: Adaptive strategies. Journal of Environmental Management, 277, 111431. https://doi.org/10.1016/j.jenvman.2020.111431\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"drought, grains, yield, association, wheat","lastPublishedDoi":"10.21203/rs.3.rs-4001110/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4001110/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe crop which is widely consumed and utilized because of its social benefits and economic importance worldwide is wheat. In many parts of the world, it is generally consumed as feed and food for its nutritional benefits, and its productivity is uniform under different environmental effects. In a randomized complete block design (RCBD) with three replication, an experiment was conducted to assess 50 different wheat cultivars. These cultivars were evaluated under two distinct levels; normal (non-stressed) and drought (stressed) conditions. The analysis of variance (ANOVA) under non-stressed (normal) and stressed (drought) conditions of the examined traits showed highly significant differences which indicated the variation in the studied germplasm. The Genotypes that performed very well in both the normal and water deficit conditions are G12 followed by G8, G23, G20 and G16 while genotypes that performed poorly in both conditions were G35 followed by, G37, G47, G48 and G42 considered as drought susceptible genotypes. Attribute grain yield per plant disclosed the significant and positive correlation among all the studied traits under the non-stresses condition. Results revealed from principal component analysis (PCA), only 4 PCA showed the significant value under the both normal and stressed conditions because these PCAs exhibited eigenvalue more than one considered as significant. Under the normal condition, the PC1 indicated the 78.7% for the variance, PC2 indicated 14.6%, PC3 indicated 12.2% and PC4 indicated 10.1% as mention in Fig-4.7. In the drought condition, the PC1 represented for 74.5%, PC2 represented for 16.7%, PC3 represented for 11.6% and PC4 represented for 11.1%. The findings from this study will be valuable for both researchers anf farmers, as they can utilize the data to cultivate these specific genotypes for improved yield. Morever, the results can also be employed in future breeding aim to develop drought-resistant wheat genotypes, contributing to sustainable food security efforts.\u003c/p\u003e","manuscriptTitle":"Assessment of Drought Tolerance in Wheat genotypes for Sustainable Food Security and Breeding Programs","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-07 15:56:27","doi":"10.21203/rs.3.rs-4001110/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0e26085b-65dd-43c4-9876-631f169dcef2","owner":[],"postedDate":"March 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-30T16:29:39+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-07 15:56:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4001110","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4001110","identity":"rs-4001110","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}