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Original article
10 2022
:34;
102279
doi:
10.1016/j.jksus.2022.102279

Grain yield, chlorophyll and protein contents of elite wheat genotypes under drought stress

, , , , , , , , , ,
a Wheat Research Institute, Faisalabad, Pakistan
b Statistical Section, Ayub Agricultural Research Institute, Faisalabad, Pakistan
c Department of Plant Breeding and Genetics, University of Agriculture, Faisalabad, Pakistan
d Oilseeds Research Institute, Ayub Agricultural Research Institute, Faisalabad, Pakistan
e Department of Agricultural Genetic Engineering, Faculty of Agricultural Sciences and Technologies, Nigde Omer Halisdemir University, Nigde, Turkey
f Department of Biological Sciences, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia
g Department of Biological Sciences, University College of Haqel, University of Tabuk, Tabuk, Saudi Arabia
h Department of Biology, College of Science, Taif University, P.O.Box 11099, Taif 21944, Saudi Arabia

⁎Corresponding author. ahsanjarid@gamil.com (Ahsan Javed)

Received: , Accepted: ,
© The Author(s) 2022
Disclaimer:
This article was originally published by Elsevier and was migrated to Scientific Scholar after the change of Publisher.

Peer review under responsibility of King Saud University.

Abstract

Background

Drought stress at different growth stages significantly alters growth, yield, and quality traits of wheat. However, great variability exists among genotypes regarding their response to drought stress. Therefore, determining the impacts of drought stress on yield and quality traits would help to select the superior genotypes.

Methods

This study investigated the effects of drought stress on wheat grain yield, chlorophyll, and protein contents. Fourteen (14) recently developed elite bread wheat (Triticum aestivum L.) genotypes were used in this study for evaluation under irrigated (full irrigation) and drought conditions (half of normal irrigation). The data relating to growth, yield and protein contents were recorded.

Results

Significant differences (P ≤ 0.01) were noted among genotypes for all recorded traits. Drought stress significantly reduced the days to 50 % heading, days to 50 % maturity, grain filling, plant height (cm), number of spikes per m2, chlorophyll index (SPAD), peduncle length (cm), number of grains spike-1, thousand grain weight (g) and grain yield (kg ha−1). However, protein contents were increased under drought stress. Correlation analysis showed significant positive association of grain yield with thousand grain weight, number of spikes per m2, spike length, chlorophyll index, grain filling period and number of grains spike-1 under both irrigated and drought stress conditions. The protein contents expressed positive and negative relationship with yield under drought stress and irrigated conditions, respectively. Bioplot analysis revealed that genotype ‘V-19618′ and ‘V-19600′ proved superior under drought conditions regarding grain yield and related traits, while genotype ‘V-19574′ proved better under both irrigated and drought conditions.

Conclusions

These identified genotypes, i.e., ‘V-19618′ and ‘V-19600′ can be utilized in future wheat breeding programs to induce desirable characters for producing drought tolerant wheat genotypes.

Keywords

Climate change
Drought stress
Chlorophyll index
Protein content
Grain yield
1

1 Introduction

Wheat (Triticum aestivum L.) is considered as major crop among cereals all over the globe. It belongs to family Poaceae with chromosome 2n = 42 (Giraldo et al., 2019). Wheat is considered as an essential part of daily human diet in different geographic regions of the world. Nearly 35 % of the global population consumes wheat as staple food. More than two-third of the global wheat production is used as food, whereas one-fifth is utilized as livestock feed (Grote et al., 2021). Wheat is a vital food source as it provides carbohydrates, fats, protein, fiber, zinc, calcium, and vitamin E etc. (Irge, 2017).

In Pakistan, wheat is cultivated as a major cereal crop, and it contributes 1.6 % towards GDP and 8.9 % share in overall agriculture. During 2020–21 ∼ 27.29 million tons wheat was produced from 9.17 million hectares in Pakistan (GOP, 2019). Globally wheat production was 780 million tons during 2021 (FAO, 2021). Wheat is consumed as staple food in Pakistan (Gul et al., 2021). Recently, wheat production has been greatly affected by climate change around the globe with the increasing unavailability of water resources. Pakistan is ranked in top ten countries which are more vulnerable to climate change (Ahmad et al., 2015). Therefore, the impacts of climate change would be quite significant on crop production in Pakistan (Kamitewoko, 2021).

Climate change is emerging as a significant threat to crop production. As the global population is growing day by day, food security is becoming the major issue of the world and setting a big challenge for the scientist to overcome this problem. Similarly, climate change is creating significant threats to fulfill the consumption demand of wheat under boosting number of population and concomitant urbanization (Anwar et al., 2020). The changing climatic is not only affecting the intensity of rainfall, but also the amount of rainfall per annum. Many areas of the country receiving < 250 mm rainfall annually (Baigal, 2016).

The grain protein concentration of wheat can be greatly influenced by abiotic stresses, which would alter baking quality (Zorb et al., 2018). Drought stress significantly affects the composition of wheat grain, including protein, gliadins, glutenin and fiber (Rakszegi et al., 2019). Wheat grain yield is a complex quantitatively inherited trait. It may be readily affected by biotic and abiotic stresses. About 25 % wheat grain yield can be enhanced by developing stress (biotic and abiotic) tolerant genotypes (Gill et al., 2004). Unfavorable environmental conditions and abiotic stresses negatively affect grain yield, resulting in considerable economic losses (Mahpara et al., 2012). Grain yield can be improved by improving source-sink association (Lawlor and Paul, 2014). Progress in grain yield can be achieved by improving crop varieties with optimum planting time. Several wheat varieties have been developed and introduced in Pakistan; however, new high-yielding cultivars are still needed to combat biotic and abiotic stresses (Sabri et al., 2020). The wheat grain yield can be estimated via its linked characteristics, i.e., number of tillers, 1000-grain weight, spike length, and number of spikelets spike-1 etc. (Li et al., 2020).

The purpose of present study was to evaluate the recently developed wheat genotypes under drought stress for identifying the key traits to aid future screening process for improving grain yield. It was hypothesized that recently developed genotypes will significantly differ in their response to drought stress and stress-free conditions. The genotypes with superior performance under drought stress would be recommended for future breeding programs to develop drought-tolerant genotypes.

2

2 Materials and Methods

2.1

2.1 Experimental site

A field experiment was performed during 2019–20 at Wheat Research Institute (WRI), Faisalabad, Pakistan (31.405286 °N, 73.048130 °E with an elevation of 184 m above sea level). The texture of soil of experimental site was sandy clay loam. The soil characteristics of the experimental site are presented in Fig. 5. The maximum total rain fall was noted during the month of March 2020 which was 143 mm as shown in Fig. 4.

2.2

2.2 Experimental design and treatments

A set of 14 genotypes of bread wheat was used in the study to investigate their behavior under irrigated and drought conditions. The variety code and parentage of fourteen genotypes elaborated in Table 1. Randomized complete block design (RCBD) was adopted with three replications in this study. The plot size for each entry was 5 m × 1.62 m (8.1 m2). All the recommended cultural and agronomic practices were adopted. The data relating to days to 50 % heading, days to 50 % maturity, grain filling duration, plant height (cm), number of spikes per m2, chlorophyll index (SPAD), peduncle length (cm), spike length (cm), number of grains spike-1, number of spikelet spike-1, thousand grain weight (g), protein content (%) and grain yield kg ha−1 were recorded.

Table 1 List of Wheat genotypes used in the experiment.
Code Genotype Parentage Type
V1 V-19503 SHORTENED SR26 TRANSLOCATION//2*WBLL1*2/KKTS/3/BECARD Adv. line
V2 V-19504 MUTUS*2/KINGBIRD #1/3/KSW/SAUAL//SAUAL Adv. line
V3 V-19521 WBLL1/KUKUNA//TACUPETO F2001/3/BAJ #1*2/4/BORL14 Adv. line
V4 V-19531 VALK/4/WBLL1*2/BRAMBLING/3/KIRITATI//PBW65/2*SERI.1B/6/WBLL1/4/BOW/NKT//CBRD/3/CBRD/5/WBLL1*2/TUKURU Adv. line
V5 V-19542 BECARD/FRNCLN//BORL14 Adv. line
V6 V-19550 ATTILA*2/PBW65//PIHA/3/ATTILA/2*PASTOR*2/6/CNO79//PF70354/MUS/3/PASTOR/4/BAV92*2/5/HAR311 Adv. line
V7 V-19554 NELOKI*2//KACHU/KIRITATI Adv. line
V8 V-19565 FRET2*2/BRAMBLING//BECARD/3/WBLL1*2/BRAMBLING*2/4/BECARD/QUAIU #1 Adv. line
V9 V-19566 INIA CHURRINCHE/KIRITATI*2//KACHU/KINDE Adv. line
V10 V-19574 IQBAL2000/DHARABI-09 Adv. line
V11 V-19589 MILAN//PRL/2*PASTOR/4/CROC1/AE.SQUARROSA(2 1 3)//PGO/3/BAV92/6/SERI.1B*2/3/KAUZ*2/BOW//KAUZ/5/CNO79//PF70354/MUS Adv. line
V12 V-19600 BABAX/LR43//BABAX/6/MOR/VEE#5//DUCULA/3/DUCULA/4/MILAN/5/BAU/MILAN/7/SKAUZ/BAV92/8/WBLLI*2/VIVITSI/3/
T.DICOCCOMP194624/AE.SQ		 Adv. line
V13 V-19602 BABAX/LR43//BABAX/6/MOR/VEE#5//DUCULA/3/DUCULA/4/MILAN/5/BAU/MILAN/7/SKAUZ/BAV92/8/WBLLI*2/VIVITSI/3
/T.DICOCCOMP194624/AE.SQ		 Adv. line
V14 V-19618 QUAIU#1/3/T.DICOCCON194625/AE.SQUARROSA(3 7 2)//3*PASTOR /4/QUAIU#2/5/KRITATI//2*PRL/2*PASTOR Adv. line

2.3

2.3 Statistical analysis

The recorded data of all the traits were analyzed using Statistix software (Version 8.1.) The means value of all the treatments were compared using Tukey’s HSD test at 5 % probability level (Steel et al., 1997). Pearson’s correlation was estimated for the identification of best correlated yield trait. Principal component analysis was performed as discussed by Curry et al. (1983) and followed by Abbas et al. (2022).

3

3 Results and discussion

3.1

3.1 Analysis of variance and means comparison of data

Highly significant differences were found among all genotypes for studied traits (Table 2). These significant differences among all traits indicated the presence of great variability that can play a vital role in grain yield enhancement of bread wheat in different breeding programs. The range of coefficient of variance was 0.4 to 4.9 % in irrigated conditions, while 0.7 to 6.4 % in drought conditions.

Table 2 Analysis of variance (ANOVA) for yield and Quality traits of different wheat genotypes under Irrigated and drought conditions.
SOV Condition df DH DG DM PH NS CC PL SL GS SP GW PC GY
Replications Irrigated 2 7.1 0.3 6.0 5.8 0.1 0.9 2.5 0.2 7.14 0.2 3.0 0.3 9245.0
Drought 5.8 27.2 7.9 34.6 33.2 10.3 0.6 0.1 2.2 0.1 0.9 0.03 1107.0
Treatments Irrigated 14 9.3** 6.5** 4.5** 95.2** 4717.5** 7.9** 23.2** 0.6** 47.9** 3.7** 39.8** 0.6** 948202.0**
Drought 9.1** 13.9** 8.9** 163.6** 5341.7** 4.9** 6.3** 0.8** 44.6** 3.3** 65.5** 0.6** 325831.0**
Error Irrigated 28 0.9 2.7 1.7 4.2 4.9 1.9 0.3 0.1 1.9 0.2 0.4 0.01 5590.0
Drought 2.4 4.6 2.1 3.0 3.6 1.9 0.3 0.01 0.6 0.2 0.2 0.02 2957.0
CV (%) Irrigated 0.9 4.5 0.9 2.1 0.4 2.8 2.9 4.9 3.4 2.5 1.4 0.5 1.5
Drought 1.4 6.4 1.0 1.9 0.7 2.7 4.2 1.6 2.7 2.9 1.2 1.1 1.9

**Significant at P ≤ 0.01; DH = days to 50 % heading, DG = grains filling duration (days), DM = days to 50 % maturity, PH = plant height (cm), NS = number of spike per m2, CC = chlorophyll index (SPAD), PL = peduncle length (cm), SL = spike length (cm), GS = number of grains spike-1, SP = number of spikelets spike-1, GW = 1000 grain weight (g), PC = protein content (%), GY = grain yield (kg ha−1).

The response of each bread wheat genotype for all the parameters under study under irrigated and drought conditions is presented in Table 3. In irrigated conditions, days to 50 % heading (DH) ranged from 112.0 to 119.0 days. The genotypes ‘V-19618′, ‘V-19574′ and ‘V-19504′ took lesser number of days for spike appearance compared to the rest of the genotypes included in the study. Under drought conditions, DH ranged from 109.0 to 115.0 days and genotypes ‘V-19600′, ‘V-19589′ and ‘V-19531′ resulted in early heading. Grain filling duration (days) varied from 34 to 39 in irrigated conditions with genotypes ‘V-19550′ and ‘V-19602′ taking lesser days to fill the grain. Grain filling duration (days) ranged from 29.0 to 37.0 days under drought stress with the genotypes ‘V-19550′ and ‘V-19554′ took less days to fill the grain. Significant variability was noted for days to 50 % maturity (DM) under both conditions. The DM varied between 150.0 and 154.0 and 142.0 to 148.0 days under irrigated and drought conditions, respectively. Genotype ‘V-19618′ under irrigated conditions and ‘V-19554′ under drought conditions proved early maturing. Plant height (PH) ranged from 85.0 to 106.0 and 79.0 to 103.0 cm under irrigated and drought conditions, respectively. Genotype ‘V-19600′ was the shortest and ‘V-19602′ was the tallest. Significant variation was found for number of tillers per m2. Number of tillers per m2 ranged from 459.0 to 570.0 and 229.3 to 362.3 under irrigated and drought conditions, respectively. Genotype ‘V-19554′ under irrigated condition and ‘V-19602′ under drought conditions produced the highest number of number of tillers per m2. The chlorophyll index (SPAD) varied from 47.0 to 52.0 and 50.0 to 54.0 in irrigated and drought conditions, respectively. Genotype ‘V-19554′ under irrigated and ‘V-19574′ under drought conditions resulted in the highest chlorophyll index. Under irrigated conditions peduncle length ranged from 14.7 to 23.8 cm and under drought condition it ranged from 11.5 to 16.0 cm. Genotype ‘V-19574′ had the highest peduncle length under both conditions.

Table 3 Means values of yield and quality traits of different wheat genotypes under Irrigated and Drought conditions.
Condition Genotype DH DG DM PH NS CC PL SL GS SP GW PC GY







Irrigated 		V-19503 114.0CD 38.0AB 152.0ABC 95.0DE 459.0 K 50.0ABC 15.3IJ 6.0CD 39.0D 16.3CD 38.4H 13.4C 4395.0F
V-19504 113.0 DE 37.0ABC 150.0C 101.0BC 496.0 G 49.0BCD 20.0D 5.5EF 38.0D 16.7BCD 42.5D 13.6B 5075.0CD
V-19521 114.0CD 37.0ABC 151.0BC 105.0A 480.0 I 48.0CD 17.3FG 6.6AB 39.0D 17.0ABC 40.5EF 13.0E 4720.0E
V-19531 113.0 DE 39.0A 152.0ABC 98.0CD 524.0 D 50.0ABC 21.0C 5.9CDE 42.0C 15.0E 45.9B 12.7F 5227.0B
V-19542 114.0CD 37.0ABC 151.0BC 103.0AB 490.7H 48.0CD 19.0E 6.1BCD 39.0D 16.3CD 43.9C 13.6B 5007.0D
V-19550 116.0B 34.0D 150.0C 99.0C 460.0 K 50.0ABC 15.8HI 6.9A 44.0BC 16.0D 39.3GH 13.5BC 4625.0E
V-19554 114.0CD 39.0A 153.0AB 93.0E 570.0 A 52.0A 23.8A 6.3ABC 46.0B 16.0D 47.6A 12.5G 5550.0A
V-19565 114.0CD 36.0BCD 150.0C 94.0E 560.0B 51.5A 22.0B 5.7DEF 38.0D 16.3CD 47.9A 13.9A 5537.0A
V-19566 115.0 BC 37.0ABC 152.0ABC 93.0E 489.3H 50.0ABC 18.0F 5.3F 39.0D 15.0E 41.4E 13.6B 4957.0D
V-19574 112.0 E 39.0A 151.0BC 95.0DE 500.0F 51.0AB 20.5CD 5.8CDE 43.0C 17.3AB 44.2C 13.2D 5167.0BC
V-19589 115.0 BC 37.0ABC 152.0ABC 94.0E 470.0 J 49.0BCD 16.7GH 5.8CDE 39.0D 14.3EF 39.8FG 12.7F 4632.0E
V-19600 114.0CD 38.0AB 152.0ABC 85.0F 510.0 E 52.0A 20.9C 5.8CDE 50.0A 17.7A 44.6C 13.2D 5187.0BC
V-19602 119.0 A 35.0CD 154.0A 106.0A 430.0 L 47.0D 14.7 J 5.3F 34.0E 14.0F 36.6I 13.4C 3352.0G
V-19618 112.0 E 38.0AB 150.0C 100.0BC 540.0C 52.0A 21.4BC 5.7DEF 42.0C 15.0E 47.3A 14.0A 5280.0B
Range 112–119 34–39 150–154 85–106 459–570 47–52 14.7–23.8 5.3–6.9 34–50 14–17.7 36.6–47.9 12.5–14 3352–5550
LSD 1.7 2.8 2.2 3.5 3.7 2.3 0.9 0.5 2.3 0.7 1.0 0.1 125.5






Drought 		V-19503 113.0ABC 34.0ABCD 147.0AB 90.0D 304.0C 52.0ABC 12.0G 6.3C 28.0F 16.0DE 38.7C 15.1DEF 3231.7B
V-19504 110.0DE 34.0ABCD 144.0CDE 102.0A 290.3E 53.0AB 15.5AB 6.1C 30.3CD 16.7BCD 34.7E 15.3D 2854.0D
V-19521 112.0BCD 32.5BCDE 144.5CD 97.0B 253.0I 51.0BC 15.0BC 5.5GH 24.7H 17.0ABC 28.5 J 15.0EFG 2552.0FG
V-19531 110.0DE 36.0AB 146.0ABC 85.0EF 254.7I 54.0A 14.0DE 5.7FG 26.7G 15.0FG 29.3I 14.8G 2628.0F
V-19542 112.0BCD 32.5BCDE 144.5CD 92.0CD 229.3 J 52.0ABC 14.5CD 5.43H 28.7EF 15.3EF 27.8 K 15.1DEF 2497.7GH
V-19550 114.0AB 29.0E 143.0DE 87.0E 214.3 K 53.0AB 12.0G 5.2I 31.0C 16.3CD 26.4L 14.9FG 2276.3I
V-19554 110.0DE 32.0CDE 142.0E 81.0G 283.0F 53.0AB 12.0G 5.9D 29.0EF 16.0DE 33.6F 15.2DE 2805.0DE
V-19565 112.0BCD 35.0ABC 147.0AB 92.0CD 276.7G 53.0AB 13.0F 5.8EF 24.3H 16.0DE 32.6G 15.8BC 2728.0E
V-19566 112.0BCD 31.0DE 143.0DE 84.0F 262.0H 52.0ABC 11.5G 5.6G 24.3H 14.7FG 30.3H 16.1A 2621.7F
V-19574 111.0CDE 33.5ABCD 144.5CD 86.0EF 347.3B 54.0A 16.0A 6.7B 33.0B 17.3AB 39.5B 15.0EFG 3240.0B
V-19589 110.0DE 36.0AB 146.0ABC 84.0F 280.3F 51.0BC 13.0F 5.9DE 29.7DE 14.7FG 33.3F 14.8G 2765.0DE
V-19600 109.0E 37.0A 146.0ABC 79.0G 294.0D 54.0A 13.5EF 6.2C 35.3A 17.7A 35.8D 15.3D 2965.7C
V-19602 115.0A 33.0BCD 148.0A 103.0A 230.3 J 50.0C 15.0BC 5.4HI 21.0I 14.3G 27.5 K 15.7C 2441.3H
V-19618 110.0DE 35.0ABC 145.0BCD 94.0C 362.3A 54.0A 14.0DE 6.8A 31.0C 15.3EF 40.6A 16.0AB 3416.7A
Range 109–115 29–37 142–148 79–103 229.3–362.3 50–54 11.5–16 5.2–6.8 21–35.3 14.3–17.7 26.4–40.6 14.8–16.1 2276.3–3416.7
LSD 2.6 3.6 2.5 2.9 3.1 2.4 0.9 0.3 1.3 0.8 0.7 0.3 91.3

DH = days to 50 % heading, DG = grains filling duration (days), DM = days to 50 % maturity, PH = plant height (cm), NS = number of spike per m2, CC = chlorophyll index (SPAD), PL = peduncle length (cm), SL = spike length (cm), GS = number of grains spike-1, SP = number of spikelets spike-1, GW = 1000 grain weight (g), PC = protein content (%), GY = grain yield (kg ha−1).

Spike length varied from 5.3 to 6.9 cm under irrigated conditions and genotype ‘V-19550′ produced the longest spikes. On the other hand, spike length under drought conditions ranged from 5.2 to 6.8 cm and genotype ‘V-19618′ produced the longest spike. Number of grains spike-1 varied from 34.0 to 50.0 and 21 to 35.3 under irrigated and drought conditions, respectively. Genotype ‘19600′ produced the highest number of grains among all genotypes under both irrigated and drought conditions. The number of spikelets per spike-1 varied from 14.0 to 17.7 under irrigated and 14.3 to 17.7 under drought conditions. Under both conditions genotype ‘V-19600′ produced the higehst number of spikelets per spike-1. Under irrigated condition thousand grain weight ranged from 36.6 to 47.9 g, while under drought it ranged from 26.4 to 40.6 g. Genotype ‘V-19565′ under irrigated conditions and ‘V-19618′ under drought conditions produced the heaviest thousand grains. Protein contents ranged from 12.5 to 14.0 % and 14.8 to 16.1 % under irrigated and drought conditions, respectively. Genotypes ‘V-19618′ had the highest protein content under both environments. Grain yield ranged between 3352 and 5550 kg ha−1 under irrigated conditions, while it varied from 2276 to 3416 kg ha−1 under drought conditions. Genotype ‘V-19554′ under irrigated conditions and ‘V-19618′ under drought stress produced the highest grain yield.

Drought stress is a major factor among other environmental stresses which can cause significant yield losses by affecting crop growth and productivity (Pour-Aboughadareh et al., 2019). Generally, drought stress leaves negative effects on physiological and agronomic characters in wheat crop (Qaseem et al., 2019). The mean data (Table 3) of wheat genotypes under irrigated and drought conditions indicated a decline in yield and related traits. Gaju et al. (2009) and Pour-Aboughadareh et al. (2020) also reported similar trend. However, protein contents under both the conditions observed a positive jump under drought stress (Kilic and Yağbasanlar, 2010). Previous studies also discussed reduction in grain yield due to drought stress (Etminan et al., 2019).

3.2

3.2 Association between measured traits

The association among recorded traits under both environments was assessed using Pearson’s correlation as adopted by Pour-Aboughadareh et al. (2020). Under drought conditions, grain yield showed highly significant positive correlation with thousand grain weight, number of spikes per m2, and spike length. On the other hand, chlorophyll index, grain filling duration, and number of grains spike-1 showed significant positive relation with grain yield. Baye et al. (2020) also reported positive correlation of grain yield with grain filling period, grains spike-1 and thousand grain weight in wheat. The higher value of chlorophyll index is prediction of good grain yield in wheat (Islam et al., 2014). The non-significant and positive correlation was found among days to 50 % maturity, protein content, peduncle length and spikelets per spike-1 with grain yield under drought condition, while non-significant negative relationship of grain yield was observed with plant height (Fig. 1). Days to 50 % heading expressed significant but negative correlation with grain yield under drought condition. Similar findings have also been explained by Mecha et al. (2017).

Phenotypic Correlation of wheat genotypes for yield and quality traits in Drought conditions (below diagonal) and Irrigated (above diagonal). CC = chlorophyll index (SPAD), DG = grains filling duration (days), DH = days to 50 % heading, DM = days to 50 % maturity, GS = number of grains spike-1, GW = 1000 grain weight (g), GY = grain yield (kg ha−1), NS = number of spike per m2, PC = protein content (%), PH = plant height (cm), PL = peduncle length (cm), SL = spike length (cm), SP = number of spikelet spike-1,
Fig. 1
Phenotypic Correlation of wheat genotypes for yield and quality traits in Drought conditions (below diagonal) and Irrigated (above diagonal). CC = chlorophyll index (SPAD), DG = grains filling duration (days), DH = days to 50 % heading, DM = days to 50 % maturity, GS = number of grains spike-1, GW = 1000 grain weight (g), GY = grain yield (kg ha−1), NS = number of spike per m2, PC = protein content (%), PH = plant height (cm), PL = peduncle length (cm), SL = spike length (cm), SP = number of spikelet spike-1,

In irrigated condition the relationship of chlorophyll index, thousand grain weight, number of spikes per m2 and peduncle length had highly significant positive correlation with grain yield, while grain filling period and number of grains spike-1 expressed significant positive correlation with grain yield. Spike length and number of spikelets spike-1 showed non-significant and positive correlation, while days to 50 % maturity, protein contents (%) and plant height showed non-significant but negative relationship with grain yield.

3.3

3.3 Principal component analysis

Principal component analysis (PCA) separated the total variance into several factors which are useful for conservation and manipulation of genetic resources and planning for utilization of appropriate germplasms in crop improvement (Zaman et al., 2014). The biplot expressed the association among different measured traits and wheat genotypes. Biplot vectors closer to each-other showed the correlation between those traits and the genotypes close to a specific trait vector represents the best performance of the wheat genotypes for that particular plant trait (Zulkiffal et al., 2018).

The biplot of PCA1 and PCA2 for irrigated (Fig. 2) and drought condition (Fig. 3) indicates the relationships between different indices. Differentiation indices in different groups was found due to PCA1 and PCA2 under both the conditions. The first and second components justified 65.4 % and 62.6 % variation between the criteria under irrigated and drought conditions, respectively.

PCA biplot of wheat Genotypes under irrigated conditions. V1 = V-19503, V2 = V-19504, V3 = V-19521, V4 = V-19531, V5 = V-19542, V6 = V-19550, V7 = V-19554, V8 = V-19565, V9 = V-19566, V10 = V-19574, V11 = V-19589, V12 = V-19600, V13 = V-19602, V14 = V-19618.
Fig. 2
PCA biplot of wheat Genotypes under irrigated conditions. V1 = V-19503, V2 = V-19504, V3 = V-19521, V4 = V-19531, V5 = V-19542, V6 = V-19550, V7 = V-19554, V8 = V-19565, V9 = V-19566, V10 = V-19574, V11 = V-19589, V12 = V-19600, V13 = V-19602, V14 = V-19618.
PCA biplot of wheat genotypes under drought conditions. V1 = V-19503, V2 = V-19504, V3 = V-19521, V4 = V-19531, V5 = V-19542, V6 = V-19550, V7 = V-19554, V8 = V-19565, V9 = V-19566, V10 = V-19574, V11 = V-19589, V12 = V-19600, V13 = V-19602, V14 = V-19618.
Fig. 3
PCA biplot of wheat genotypes under drought conditions. V1 = V-19503, V2 = V-19504, V3 = V-19521, V4 = V-19531, V5 = V-19542, V6 = V-19550, V7 = V-19554, V8 = V-19565, V9 = V-19566, V10 = V-19574, V11 = V-19589, V12 = V-19600, V13 = V-19602, V14 = V-19618.
Meteorological data of the experimental site during 2019–20.
Fig. 4
Meteorological data of the experimental site during 2019–20.
Characteristics of soil of the field trial site.
Fig. 5
Characteristics of soil of the field trial site.

The PCA biplot under irrigated conditions (Fig. 2) revealed positive association of grain yield with chlorophyll index, thousand grain weight, number of spikes per m2, peduncle length, grain filling duration, number of grains spike-1, spike length (cm) and number of spikelets spike-1. However, plant height (cm), protein contents (%), days to 50 % maturity and 50 % heading expressed negative relationship with grain yield. Genotype ‘V-19574′ proved the best genotype under irrigated conditions regarding grain yield and related traits.

Under drought stress, PCA biplot showed positive relationship of grain yield with thousand grain weight, number of spikes per m2, spike length (cm), chlorophyll index, grain filling period and number of grains spike-1. On the other hand, negative relationship was found regarding days to 50 % heading and 50 % maturity, protein content, peduncle length, and number of spikelets spike-1. Genotypes ‘V-19618′, ‘V-19574′ and ‘V-19600′ proved best genotypes under drought conditions regarding grain yield and related traits.

4

4 Conclusion

The current findings revealed that wheat genotypes were considerably affected by drought stress for grain yield and morpho-physiological traits. There was notable variation was found in the recorded traits and these genotypes may be considered for wheat breeding programs for drought tolerance. As seen in the current results, positive relationship of thousand grain weight, number of spikes per m2, spike length, chlorophyll index, grain filling duration and number of grains spike-1 with grain yield made the wheat genotypes to perform better in drought stressed environment. The protein content was negatively associated with yield in irrigated conditions but increase in these contents was recorded under drought stress. Among all tested genotypes, ‘V-19574′ proved better under both (irrigated and drought) conditions regarding grain yield and its contributing traits. It can be utilized in drought prone environment for cultivation and to develop drought tolerant varieties.

Acknowledgements

Authors would like to thank Taif University Researchers Supporting Project number (TURSP-2020/64), Taif University, Taif, Saudi Arabia.

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.

References

  1. K. Abbasa, Z. Hussainb, M. Hussaina, F. Rahimc, N. Ashrafd, Q. Khane, G. Razaf, A. Alig, D. M. Khanh, U. Khalilh, N. Irshad. 2022. Statistical modeling for analyzing grain yield of durum wheat under rainfed conditions in Azad Jammu Kashmir, Pakistan. Brazil J. Biol., 2022, vol. 82, e240199 Doi: 10.1590/1519-6984.240199.
  2. , , , , , , , , , , . Impact of climate change on the rice–wheat cropping system of Pakistan. In: , , eds. Handbook of Climate Change and Agroecosystems: The Agricultural Model Intercomparison and Improvement Project Integrated Crop and Economic Assessments. London: Imperial College Press; . p. :219-258.
    [Google Scholar]
  3. , , , . Impact of urbanization and economic growth on CO2 emission: A case of far east Asian countries. Int. J. Environ. Res.. 2020;17:2531.
    [CrossRef] [Google Scholar]
  4. Birhanu Mecha, Sentayehu Alamerew, Alemayehu Assefa, Ermias Assefa, Dargicho Dutamo. 2017. Correlation and path coefficient studies of yield and yield associated traits in bread wheat (Triticum aestivum L.) Genotypes. Adv. Plants Agric. Res. 2017;6(5):128-136. DOI: 10.15406/apar.2017.06.00226.
  5. Baigal, P.M., 27 April, 2016. accessed. https://www.thethirdpole.net/en/climate/climate- change-brings-severe-risks-to-northern-pakistan/2016.
  6. , , , , , , , . The principal components of auditory target detection. Adv. Psychol.. 1983;10:79-117.
    [CrossRef] [Google Scholar]
  7. , . Chemical Composition and Nutritional Quality of Wheat, Teff Eragrostis tef (Zucc) Trotter), Barley (Hordeum vulgare L.) and Rice (Oryza sativa) -A Review. Food Sci. Qual. Manage.. 2017;59:6-11.
    [Google Scholar]
  8. , , , , , , . Determining the best drought tolerance indices using artificial neural network (ANN): Insight into application of intelligent agriculture in agronomy and plant breeding. Cereal Res. Commun.. 2019;47:170-181.
    [Google Scholar]
  9. , , , , . Relationships between large-spike phenotype, grain number, and yield potential in spring wheat. Crop Sci.. 2009;49:961-973.
    [Google Scholar]
  10. , , , , , , , , , , , , , , , . Pooled analysis of fluorouracil-based adjuvant therapy for stage II and III colon cancer: who benefits and by how much. J. Clin. Oncol.. 2004;22(10):1797-1806.
    [Google Scholar]
  11. , , , , . Worldwide Research Trends on Wheat and Barley: A Bibliometric Comparative Analysis. Agronomy.. 2019;9:1-18.
    [Google Scholar]
  12. Government of Pakistan. 2019. Economic survey of Pakistan. Statistics of Pakistan.Ministry of National Food Security and Research (Economic Wing), Islamabad.
  13. , , , , . Food Security and the Dynamics of Wheat and Maize Value Chains in Africa and Asia. Front. Sustain. Food Syst.. 2021;4(4):617009
    [CrossRef] [Google Scholar]
  14. , , , , , . How climate change is impacting the major yield crops of Pakistan? an exploration from long-and short-run estimation. Environ. Sci. Pollut. Res. 2021:1-15.
    [Google Scholar]
  15. , . Impact of Climate Change on Food Crop Production in Congo Brazzaville. Modern Econ.. 2021;12(11):1686-1702.
    [Google Scholar]
  16. , , . The effect of drought stress on grain yield, yield components and some quality traits of durum wheat (Triticum turgidum ssp. durum) cultivars. Notulae Botanicae Horti Agrobotanici Cluj-Napoca. 2010;38(1):164-170.
    [Google Scholar]
  17. , , . Source/sink interactions underpin crop yield: the case for trehalose 6-phosphate/SnRK1 in improvement of wheat. Front. Plant Sci.. 2014;5:418.
    [Google Scholar]
  18. , , , , , , , , , , , , , , , . Characterization of a major quantitative trait locus on the short arm of chromosome 4B for spike number per unit area in common wheat (Triticum aestivum L.) Theor. Appl. Genet.. 2020;133(7):2259-2269.
    [Google Scholar]
  19. , , , , , . Assessment of genetic distance among wheat genotypes through RAPD markers. Adv. Agric. Bot.. 2012;4:31-35.
    [Google Scholar]
  20. Alireza Pour-Aboughadareh 1, Reza Mohammadi 2, Alireza Etminan 3, Lia Shooshtari 4, Neda Maleki-Tabrizi 5 and Peter Poczai.2020. Effects of Drought Stress on Some Agronomic and Morpho-Physiological Traits in Durum Wheat Genotypes. Sustainability. 12, 5610; doi:10.3390/su12145610 pp. 1-14.
  21. , , , , , , , . Effect of water deficit stress on seedling biomass and physio-chemical characteristics in different species of wheat possessing the D genome. Agronomy. 2019;9:522.
    [Google Scholar]
  22. , , , . Heat and their combination on the growth, yield and physiology of diverse wheat (Triticum aestivum L.) genotypes varying in sensitivity to heat and drought stress. Sci. Rep.. 2019;9:6955.
    [Google Scholar]
  23. , , , , , , . Drought stress affects the protein and dietary fiber content of whole meal wheat flour in wheat/Aegilops addition lines. PLoS ONE. 2019;14(2):1-19.
    [CrossRef] [Google Scholar]
  24. , , , , , , . Assessment of agro-morphologic performance, genetic parameters and clustering pattern of newly developed blast resistant rice lines tested in four environments. Agronomy. 2020;10:1098.
    [Google Scholar]
  25. Steel, R.G.D.; Torrie, J.H.; Dickey, D.A. Principles and Procedures of Statistics. A Biometrical Approach; McGraw Hill Book Co.: New York, NY, USA, 1997; pp. 400–428.
  26. , , , , . Divergence analysis of drought tolerant. Bangladesh Journal of Agriculture Research. 2014;39:385-396.
    [Google Scholar]
  27. , , , . Perspective on wheat yield and quality with reduced nitrogen supply. Trends Plant Sci.. 2018;23:1029-1037.
    [Google Scholar]

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