







































 
 

 

30 
© 2024 by the authors; licensee Asian Online Journal Publishing Group 
 

Agriculture and Food Sciences Research 
Vol. 11, No. 1, 30-35, 2024 

ISSN(E) 2411-6653/ ISSN(P) 2518-0193 
DOI: 10.20448/aesr.v11i1.5486 

© 2024 by the authors; licensee Asian Online Journal Publishing Group 

 
 

 
 
 
Agro-morphological analysis of yield and yield attributing traits of wheat under 
heat stress condition 

 
Pragyan Bhattarai1   

Prashant Gyanwali2   

Netra Prasad Pokharel3   

Parbin Bashyal4   

Rasmita Mainali5   

Renuka Khanal6   

 

 
( Corresponding Author) 

 
1,2,3,4,5,6Institute of Agriculture and Animal Science, Paklihawa Campus, Rupandehi, Tribhuvan University, Nepal. 
1Email: pragyanbhattarai29@gmail.com  
2Email: Prashantgyawali7@gmail.com  
3Email: netrapokharel73@gmail.com  
4Email: parbinbashyal56@gmail.com  
5Email: 2057mainalirasmita@gmail.com  
6Email: Renukhanal57@gmail.com  

 
Abstract 

Wheat is the most important cereal crop worldwide and ranks third in Nepal. Improvements in 
wheat yield can be done effectively by selection for yield attributing traits. In this experiment, 
twenty wheat genotypes were evaluated in the terai region of Nepal at Paklihawa, Rupandehi in 
Alpha lattice design under heat stress conditions. The characters were evaluated to find their 
correlation and direct and indirect effects on yield. Positive significant correlation of grain yield 
with No. of spikes m-2 (0.405) and harvest index (0.647) were found whereas Spike weight (-
0.322) showed a significant negative correlation with grain yield. Similarly, Path analysis showed 
that the Harvest index (0.5511) and No. of spikelets per spike (0.3365) had a high direct effect, 
whereas Thousand kernel weight, Spike m-2, and Plant height showed a lower positive direct 
effect on grain yield. Ten spikes weight, spike length, and No. of grains per spike showed low 
negative direct effects. The conclusions drawn from this analysis can be useful for breeding 
programs under heat stress by providing information on which characteristics significantly affect 
the yield. However, multi-locations and multi-year trials need to be done for further verifications 
on the selection of such traits for improving yield. 

 
Keywords: Correlation, Heat-stress, Path-coefficient analysis, Plant breeding, Selection, Wheat parameters. 

 
Citation | Bhattarai, P., Gyanwali, P., Pokharel, N. P., Bashyal, P., 
Mainali, R., & Khanal, R. (2024). Agro-morphological analysis of 
yield and yield attributing traits of wheat under heat stress 
condition. Agriculture and Food Sciences Research, 11(1), 30–35. 
10.20448/aesr.v11i1.5486 
History:  
Received: 27 December 2023 
Revised: 30 January 2024 
Accepted: 15 February 2024 
Published: 21 March 2023 
Licensed: This work is licensed under a Creative Commons 

Attribution 4.0 License  
Publisher:  Asian Online Journal Publishing Group 
 

Funding: This research is supported by Institute of Agriculture and Animal 
Science, Pakhlihawa Campus, Tribhuvan University (Grant number: 21-1104). 
Institutional Review Board Statement: The Ethical Committee of the 
Institute of Agriculture and Animal Science, Pakhlihawa Campus, Tribhuvan 
University, Nepal has granted approval for this study on 21 August 2023 (Ref. 
No. 15 0823). 
Transparency: The authors confirm that the manuscript is an honest, 
accurate, and transparent account of the study; that no vital features of the 
study have been omitted; and that any discrepancies from the study as planned 
have been explained. This study followed all ethical practices during writing. 
Competing Interests: The authors declare that they have no competing 
interests. 
Authors’ Contributions: All authors contributed equally to the conception 
and design of the study. All authors have read and agreed to the published 
version of the manuscript. 

 

Contents 
1. Introduction ...................................................................................................................................................................................... 31 
2. Materials and Methods ................................................................................................................................................................... 31 
3. Result and Discussion ..................................................................................................................................................................... 32 
4. Conclusion ......................................................................................................................................................................................... 34 
References .............................................................................................................................................................................................. 34 
 

 

 

mailto:pragyanbhattarai29@gmail.com
mailto:Prashantgyawali7@gmail.com
mailto:netrapokharel73@gmail.com
mailto:parbinbashyal56@gmail.com
mailto:2057mainalirasmita@gmail.com
mailto:Renukhanal57@gmail.com
https://creativecommons.org/licenses/by/4.0/
https://creativecommons.org/licenses/by/4.0/
https://www.doi.org/10.20448/aesr.v11i1.5486
https://orcid.org/0009-0001-9687-4479
https://orcid.org/0000-0001-6428-491X
https://orcid.org/0009-0001-9237-3992
https://orcid.org/0009-0002-2929-0868
https://orcid.org/0009-0006-7150-5577
https://orcid.org/0009-0001-6929-4284


Agriculture and Food Sciences Research, 2024, 11(1): 30-35 

31 
© 2024 by the authors; licensee Asian Online Journal Publishing Group 

 

 

Contribution of this paper to the literature 
This paper builds on the existing knowledge of wheat breeding and helps guide wheat breeding 
by providing information regarding the major parameters that have the maximum effect on 
wheat yield. It helps highlight the parameters to look out for and prioritize during selection. 

 
1. Introduction 
1.1. Background 

Wheat is one of the most important crops in the world, providing a staple diet for 2 billion people [1]. It 
accounts for 28% of world cereal production and 41.5% of all cereal trade globally [2]. It was cultivated on 215.9 
million hectares worldwide in 2019, yielding 765.8 million tonnes with productivity of 3.54 mt/ha [3]. Wheat is 
the third most cultivated cereal after rice and maize in Nepal. The area, productivity, and production of wheat in 
Nepal are 711ha, 2.99 mt/ha, and 2,127,276 mt respectively [4]. The wheat productivity in Nepal is far behind 
compared to a nation like New Zealand with a high productivity status [5].  

Wheat accounts for about 55% of all carbohydrates and 20% of all food calories consumed worldwide [1]. The 
demand for wheat is expected to rise by 60% in 2050 [6]. Increment in wheat production by 2% annually is needed 
to fulfill this demand [7]. The developing nations should increase their wheat production by 77% to meet future 
demands [8]. This emphasizes various crop improvement practices for increment of both production and 
productivity as a necessity of the 21st century [9]. This can be accomplished by producing high-yielding, climate-
smart, and stress-tolerant varieties of wheat by intense selection under field circumstances [10]. 

The average global temperature has increased by 1.04°C between 1880 to 2019 [11]. The temperature is 
estimated to increase further by 1.5°C in the next twenty years [12]; so, global wheat production is predicted to 
fall by 6% for every 1°C rise in temperature [13]. The impact of Heat stress is determined by the length and 
severity of the stress, along with genetic factors [14]. Terminal heat stress in wheat is observed when the mean 
temperature exceeds 31°C at the grain-filling stage [15]. Increment of 1°C average temperature during March and 
April causes a seven-day reduction in the length of the wheat crop and a yield decrease of around 400 kg/ha 
[16]. The Terai region of Nepal has a significant temperature increase starting in mid-March, which coincides 
with anthesis and is subject to scorching winds from the west [17]. It was reported that the maximum temperature 
rose by 1 °C in the Terai region of Nepal over the previous 25 years which will be detrimental to wheat cultivation 
in the future [18].  

Yield is a complex quantitative trait that is considerably affected by, and depends on the environment. The 
selection of genotypes based on various components of yield is effective in comparison to the selection regarding 
yield alone [19]. The study aims to understand the relationship between various components of 20 wheat 
genotypes and the yield in heat stress conditions. The associations will help determine the most important 
characters affecting the yield and set a standard selection guide of traits to look out for selection.  
 

1.2. Statement of Problem 
The major physiological functions like chlorophyll degradation and photosynthesis are affected by heat stress 

condition [20]. The grain filling period , pollen abortion, poor seed set are also observed under this condition 
[21]. It is estimated that a 1°C increase in temperature would result in a 6% decrease in global wheat yield [5]. 
Increased temperature in the future will hugely impact the yield of wheat worldwide [22]. Research activities 
exploring the relationship between yield and yield attributing characters among various genotypes have been 
conducted by numerous researchers, but only a few have been conducted under a heat-stress environment, 
especially in the western region of Nepal. This will help us in creating an efficient selection of genotypes under heat 
stress condition. 
 

1.3. Rationale of Study 
Heat stress resulted in a total of 36.03 billion in wheat yield loss in the year 2020 in the context of Nepal [9]. 

The development of wheat genotypes with high yield potential and the capacity to sustain output in all conditions, 
including heat stress, is one of the key objectives of wheat breeders. But, the practice of unilateral selection for 
agronomic features and inadequate understanding of the interactions between multiple characters usually leads to 
unsatisfactory results in wheat breeding.  Hence, this study is performed to determine which characteristics have a 
significant impact on wheat output. Plant breeders would then be able to prioritize these traits for further yield 
enhancement, as the study of yield-contributing components concerning their genetic mechanism is very important 
for yield improvement.  
 

2. Materials and Methods  
Twenty wheat genotypes including 15 Nepal-lines (NL), 3 Bhairahawa-lines (BL) and 2 check varieties i.e. 

Bhrikuti and Gautam, were collected from National Wheat Research Program, Bhairahawa. The research was 
conducted in Paklihawa campus, Rupandehi, during 2021-2022.  

Yield and yield attributing traits like Plant height (Ph), Spike length (SL), Spike weight (SW), Spikes per m2 

area (SPMS), Number of grains per spike (NGPS), Number of spikelets per spike (NSPS), Thousand kernels weight 
(TKW) and Harvest Index (HI) were noted from ten randomly selected plants per plot.  

Agro-meteorological data were recorded at the experimental site from 26th December 2021 to 17th April 2022, 
and average was found as in Figure 1. Line sowing of seeds was done on 26th December, so as to reach the 
reproductive stage of wheat in the hot weather of April inducing heat stress condition.  
 



Agriculture and Food Sciences Research, 2024, 11(1): 30-35 

32 
© 2024 by the authors; licensee Asian Online Journal Publishing Group 

 

 

 
Figure 1. Agro-meteorological data of temperature and relative humidity recorded at the site of experiment through crop period. 

 
The experiment was carried out in alpha-lattice design replicated twice with five blocks Figure 2. One meter 

space was maintained between two replications. The plot size was maintained at 4m*2.5m each.  
 

 
Figure 2. (Alpha lattice design) layout of the field. 

 
Data entry was done by using Microsoft Excel Spreadsheet Software. SPSS version 25 and Microsoft Excel 

were used to perform co-relation and path coefficient analysis. 
 

3. Result and Discussion 
3.1. Correlation 

The association of grain yield and yield attributing traits are presented in Table 1. Plant height showed a 
positive correlation with NSPS, SW, SPMS and a negative correlation with NGPS, TKW, GY, and HI. Dwarf 
wheat varieties increase lodging resistance which leads to higher nitrogen use efficiency. Wheat plants tend to use 
stem reserve when they are under heat stress thus developing a positive correlation with yield [23]. It was found 
that selecting dwarf plants with thick stems may boost yield when coping with late heat stress [23]. Plant height 
was found to have a positive correlation with all studied traits except maturity, grain yield, and harvest index for 
both genetic and phenotypic levels [24].  

Number of grains per spike showed a highly positive significant correlation with NSPS (0.68), and SW (0.58) 
and a non-significant positive correlation with SL, and SPMS. It showed a significant negative correlation with 
TKW (-0.33) and a non-significant negative correlation with PH, GY, and HI. It was found that NGPS showed a 
positive and highly significant correlation with SW and NSPS [25]. 
 
 
 
 



Agriculture and Food Sciences Research, 2024, 11(1): 30-35 

33 
© 2024 by the authors; licensee Asian Online Journal Publishing Group 

 

 

Table 1. Correlation of different wheat parameters. 

Parameters Ph NGPS NSPS SL SW TKW GY SPMS HI 

Ph 1 -0.044 0.071 0.299 0.125 -0.232 -0.026 0.026 -0.114 
NGPS -0.044 1 0.685** 0.290 0.587** -0.330* -0.177 0.045 -0.216 
NSPS 0.071 0.685** 1 0.442** 0.460** -0.212 0.037 0.177 -0.175 
SL 0.299 0.290 0.442** 1 0.510** 0.104 -0.172 -0.222 -0.091 
SW 0.125 0.587** 0.460** 0.510** 1 0.119 -0.322* -0.293 -0.252 
TKW -0.232 -0.330* -0.212 0.104 0.119 1 0.229 -0.160 0.263 
GY -0.026 -0.177 0.037 -0.172 -0.322* 0.229 1 0.405** 0.647** 
SPMS 0.026 0.045 0.177 -0.222 -0.293 -0.160 0.405** 1 0.123 
HI -0.114 -0.216 -0.175 -0.091 -0.252 0.263 0.647** 0.123 1 
Note:  
 

Ph= Plant height, NGPS= Number of grains per spike, NSPS= Number of spikelets per spike, SL= Spike length, SW= Ten spikes weight, TKW= 
Thousand kernels weight, GY= Grain yield, SPMS= Number of spikes per m2. 
** Correlation is significant at the 0.01 level (2-tailed). 
* Correlation is significant at the 0.05 level (2-tailed). 

 
Number of spikelets per spike showed a significantly high positive correlation with SL (0.44), SW (0.46), and 

NGPS (0.68) and a non-significant positive correlation with PH, GY, and SPMS. It showed a negative correlation 
with TKW, and HI. A similar relationship of NSPS with SW and NGPS was found by Thapa, et al. [25].  

Spike length showed a significantly high positive correlation with NSPS (0.44) and SW (0.51). It showed a non-
significant positive correlation with PH, NGPS, and TKW. It exhibited a negative correlation with GY, SPMS, and 
HI. It was shown that SL had a positive and highly significant correlation with SW but in contrast, it had a 
negative and highly significant correlation with NSPS [25]. 

 Spike weight showed a highly significant positive correlation with NGPS (0.58), NSPS (0.46), and SL (0.51) 
and a non-significant positive correlation with TKW, and PH. It showed a significant negative correlation with GY 
(-0.32) and non-significant negative correlation with HI. Heat stress accelerates the spike development process 
resulting in fewer grains and reduced harvest index [26]. 

Thousand kernel weight showed a significantly negative correlation with NGPS (-0.33) and a non-significant 
negative correlation with PH, NSPS, and SPMS. It showed a positive correlation with SL, SW, GY, and HI. There 
is a decrement in the life cycle of the wheat crop under heat stress [26]. This results in a reduction of the grain 
filling period [27]. This lowers the thousand kernel weight and grain size, degrading the quality of the seed and its 
yield [28].  

Grain yield showed a significantly negative correlation with SW (-0.32) and a non-significant negative 
correlation with PH, NGPS, and SL. It showed a highly significant positive correlation with SPMS (0.40) and HI 
(0.64) and a non-significant positive correlation with TKW. In another research, it was found that grain yield 
showed a significantly positive correlation with harvest index and spike per meter square which is similar to our 
finding [29].  

Number of spikes per m2 showed a negative correlation with SL, SW, and TKW. It showed a highly significant 
positive correlation with GY (0.40) and a non-significant positive correlation with PH, NGPS, NSPS, and HI.  

Harvest Index showed a negative correlation with PH, NGPS, NSPS, SL, and SW. It showed a highly 
significant positive correlation with GY (0.64) and a non-significant positive correlation with TKW, and SPMS. It 
was found that the Harvest index showed a significant negative correlation with NSPS but in contrast, it showed a 
significant negative correlation with grain yield [30]. 
 

3.2. Path Coefficient Analysis 
The path coefficient analysis helps us determine the direct and indirect effects of the yield parameters and 

quantifies their role over one another.  The positive and negative values of parameters determine the direction of 
yield, whether it increases or decreases when there are changes in the parameters.  
 
Table 2. Path Coefficient analysis of different wheat parameters. 

Parameters 
Ph NGPS NSPS SL SW TKW SPMS HI 

Correlation 
with yield 

Via Ph 0.146 -0.006 0.01 0.044 0.018 -0.034 0.004 -0.017 -0.026 

Via NGPS 0.001 -0.026 -0.018 -0.008 -0.016 0.009 -0.001 0.006 -0.177 

Via NSPS 0.024 0.23 0.336 0.149 0.155 -0.071 0.06 -0.059 0.037 

Via SL -0.052 -0.05 -0.077 -0.174 -0.089 -0.018 0.039 0.016 -0.172 

Via SW -0.028 -0.129 -0.101 -0.112 -0.22 -0.026 0.065 0.055 -0.322 

Via TKW -0.06 -0.085 -0.055 0.027 0.031 0.259 -0.041 0.068 0.229 

Via SPMS 0.005 0.009 0.038 -0.047 -0.063 -0.034 0.213 0.026 0.405 

Via HI -0.062 -0.119 -0.096 -0.05 -0.139 0.145 0.068 0.551 0.647 
Note:  Ph= Plant height, NGPS= Number of grains per spike, NSPS= Number of spikelets per spike, SL= Spike length, SW= Ten spikes weight, 

TKW= Thousand kernels weight, GY= Grain yield, SPMS= Number of spikes per m2. 
 

Direct effect of yield attributing character (bold in Table 2) showed that HI had the highest positive direct 
effect (0.55114) on the grain yield. Following the HI, exhibiting the second highest positive direct effect is NSPS 
(0.33647). Likewise, TKW (0.25882), SPMS (0.21340), and Ph (0.14572) had similar direct positive effects. 
Confirmatory similar positive results were also obtained by Meena, et al. [31]. The other parameters showed a 
negative direct effect on yield, where NGPS showed the lowest direct effect (-0.0266), followed by SL (-0.17366) 
and SW (-0.22019). Contradicting results regarding Ph and SL were obtained by Mohanty, et al. [23]. Likewise, 
positive direct effects by NGPS and Ph on grain yield were obtained by Oliveira, et al. [32]. The direct effects have 
a greater possibility of determining the outcome while selecting the yield as they do not depend upon the other 
parameters. The traits or parameters whose direct effect value is close to the correlation coefficient value, usually 
have little to no indirect effect on the grain yield. This suggests that selection for such traits is beneficial and much 
more effective in observing the improved grain yield [33]. The traits having positive correlation with the yield but 



Agriculture and Food Sciences Research, 2024, 11(1): 30-35 

34 
© 2024 by the authors; licensee Asian Online Journal Publishing Group 

 

 

no positive direct effect is not considered while selecting for the improved yield since other traits are responsible 
for the yield indirectly [34]. 

The indirect effects weren’t as pronounced as the direct effects, with the majority having a nearly negligible 
magnitude. The highest positive indirect effect on yield was shown by NGPS via NSPS (0.23048), followed by SW 
via NSPS (0.15478), SL via NSPS (0.14872), and TKW via HI (0.14495). The highest negative indirect effect on 
yield was shown by SW via HI (-0.13889), followed by NGPS via SW (-0.12925), NGPS via HI (-0.11905), SL via 
SW (-0.1123), and NSPS via SW (-0.10129). The other indirect effects on yield had low values. Partial similar 
results were obtained by Kayastha, et al. [35]. 
 

4. Conclusion 
The grain yield has a positive correlation with SPMS and harvest index, which also showed high direct effects 

through the path coefficient analysis. Therefore, selection should be done in favor of these yield parameters to 
increase the overall yield of the wheat crop in the given environment condition. Grain yield has a negative 
correlation with spike weight, plant height, number of grains per spike & spike length. The path coefficient analysis 
showed the highest negative direct effects on grain yield by spike weight. Therefore, it is suggested that we avoid 
selecting genotypes with higher value of SW during selection. The indirect effects on grain yield weren’t profound 
in magnitude as the direct effects, however, the highest positive and negative indirect effects were shown by the 
number of grains per spike via number of spikelets per spike, and spike weight via harvest index. 
 

References 
[1] A. K. Maurya, R. Yadav, A. K. Singh, A. Deep, and V. Yadav, "Studies on correlation and path coefficients analysis in bread wheat 

(Triticum aestivumL.)," Journal of Pharmacognosy and Phytochemistry, vol. 9, no. 4, pp. 524-527, 2020.  
[2] FAO, "Crop and livestock products," Retrieved: http://www.fao.org/faostat/en/#data/QC. 2020.  
[3] FAO, "Crop prospects and food situation," Quarterly Global Report No. 1, no. March. Rome, 2020.  
[4] MoALD, "Statistical information on nepalese agriculture (2077/78), Gov. Nepal, Kathmandu, Nepal," Retrieved: 

https://nepalindata.com/resource/statistical-information-nepalese-agriculture-207374-201617/. 2022.  
[5] M. R. Poudel, P. B. Poudel, R. R. Puri, and H. K. Paudel, "Variability, correlation and path coefficient analysis for agro-

morphological traits in wheat genotypes (Triticum aestivum L.) under normal and heat stress conditions," International Journal of 
Applied Sciences and Biotechnology, vol. 9, no. 1, pp. 65-74, 2021.  https://doi.org/10.3126/ijasbt.v9i1.35985 

[6] FAO, World food and agriculture - statistical pocketbook. Rome, Italy: FAO. https://doi.org/10.4060/CA1796EN, 2018. 
[7] I. Al-Ashkar, M. Alotaibi, Y. Refay, A. Ghazy, A. Zakri, and A. Al-Doss, "Selection criteria for high-yielding and early-flowering 

bread wheat hybrids under heat stress," PloS One, vol. 15, no. 8, p. e0236351, 2020.  https://doi.org/10.1371/journal.pone.0236351 
[8] I. Sharma, B. Tyagi, G. Singh, K. Venkatesh, and O. Gupta, "Enhancing wheat production-A global perspective," The Indian 

Journal of Agricultural Sciences, vol. 85, no. 1, pp. 3-13, 2015.  https://doi.org/10.56093/ijas.v85i1.45935 
[9] P. B. Poudel and M. R. Poudel, "Heat stress effects and management in wheat," A Review Journal of Biology and Today's World, vol. 

9, no. 4, p. 217, 2020.  
[10] M. W. Riaz et al., "Effects of heat stress on growth, physiology of plants, yield and grain quality of different spring wheat 

(Triticum aestivum L.) genotypes," Sustainability, vol. 13, no. 5, pp. 1-18, 2021.  https://doi.org/10.3390/su13052972 
[11] National Oceanic and Atmospheric Administration (NOAA), "Noa a F Y 2 2-26 S Tr ategic Pl a N climate ready nation building a," 

Retrieved: https://www.noaa.gov/. 2020.  
[12] IPCC, Climate change 2021: The physical science basis. Cambridge University Press. https://doi.org/10.1017/9781009157896, 2021. 
[13] S. Asseng, I. Foster, and N. C. Turner, "The impact of temperature variability on wheat yields," Global Change Biology, vol. 17, no. 

2, pp. 997-1012, 2011.  https://doi.org/10.1111/j.1365-2486.2010.02262.x 
[14] R. R. Kumar et al., "Silicon triggers the signalling molecules and stress-associated genes for alleviating the adverse effect of 

terminal heat stress in wheat with improved grain quality," Acta Physiologiae Plantarum, vol. 44, no. 3, pp. 1-17, 2022.  
https://doi.org/10.1007/s11738-022-03365-y 

[15] R. Dubey, H. Pathak, B. Chakrabarti, S. Singh, D. K. Gupta, and R. Harit, "Impact of terminal heat stress on wheat yield in India 
and options for adaptation," Agricultural Systems, vol. 181, p. 102826, 2020.  https://doi.org/10.1016/j.agsy.2020.102826 

[16] K. Singh, S. Sharma, and Y. Sharma, "Effect of high temperature on yield attributing traits in bread wheat," Bangladesh Journal of 
Agricultural Research, vol. 36, no. 3, pp. 415-426, 2011.  

[17] R. R. Puri, N. R. Gautam, and A. K. Joshi, "Exploring stress tolerance indices to identify terminal heat tolerance in spring wheat in 
Nepal," Journal of Wheat Research, vol. 7, no. 1, pp. 13-17, 2015.  https://doi.org/10.1515/cerce-2015-0004 

[18] K. R. Dahal, R. R. Puri, and A. K. Joshi, "Effect of climate change and associated factors on the production and productivvity of 
wheat (Triticum aestivum L.) over last 25 years in the terai region of Nepal," International Journal of Environmental Research and 
Public Health, vol. 4, no. 3, pp. 151–165, 2015.  

[19] N. Khan and F. Naqvi, "Correlation and path coefficient analysis in wheat genotypes under irrigated and non-irrigated conditions," 
Asian Journal of Agricultural Sciences, vol. 4, no. 5, pp. 346-351, 2012.  

[20] P. Gyanwali and R. Khanal, "Effect of drought stress in morphology, phenology, physiology and yield of Wheat," Plant Physiology 
and Soil Chemistry, vol. 1, no. 2, pp. 45–49, 2021.  https://doi.org/10.26480/ppsc.02.2021.45.49 

[21] A. Hossain, M. Islam, K. Rahman, M. Rashid, and A. Anwari, "Comparative performance of three wheat (Triticum aestivum L.) 
varieties under heat stress," International Journal of Natural and Social Sciences, vol. 4, no. 3, pp. 26-42, 2017.  

[22] A. Hossain, J. A. T. da Silva, M. V. Lozovskaya, and V. P. Zvolinsky, "The effect of high temperature stress on the phenology, 
growth and yield of five wheat (Triticum aestivum L.) varieties," Asian and Australasian Journal of Plant Science and Biotechnology, 
vol. 6, no. 1, pp. 14-23, 2012.  https://doi.org/10.15835/nsb437879 

[23] S. Mohanty, S. Mukherjee, S. Mukhopadhyaya, and A. Dash, "Genetic variability, correlation and path analysis of bread wheat 
(Triticum aestivum L.) genotypes under terminal heat stress," International Journal of Bio-resource and Stress Management, vol. 7, no. 
6, pp. 1232-1238, 2016.  https://doi.org/10.23910/ijbsm/2016.7.6.1666a 

[24] K. Upadhyay, "Correlation and path coefficient analysis among yield and yield attributing traits of wheat (Triticum aestivum L.) 
genotypes," Archives of Agriculture and Environmental Science, vol. 5, no. 2, pp. 196-199, 2020.  
https://doi.org/10.26832/24566632.2020.0502017 

[25] A. Thapa, S. Jaisi, and M. R. Poudel, "Genetic variability and association among yield and yield components of wheat genotypes 
(Triticum aestivum L.)," Big Data in Agriculture, vol. 4, no. 1, pp. 01-07, 2022.  https://doi.org/10.26480/bda.01.2022.01.07 

[26] A. Ullah, F. Nadeem, A. Nawaz, K. H. Siddique, and M. Farooq, "Heat stress effects on the reproductive physiology and yield of 
wheat," Journal of Agronomy and Crop Science, vol. 208, no. 1, pp. 1-17, 2022.  https://doi.org/10.1111/jac.12572 

[27] J. M. Arjona, C. Royo, S. Dreisigacker, K. Ammar, J. Subirà, and D. Villegas, "Effect of allele combinations at Ppd‐1 loci on durum 
wheat grain filling at contrasting latitudes," Journal of Agronomy and Crop Science, vol. 206, no. 1, pp. 64-75, 2020.  
https://doi.org/10.1111/jac.12363 

[28] Y. Zhang et al., "Differential effects of a post-anthesis heat stress on wheat (Triticum aestivum L.) grain proteome determined by 
iTRAQ," Scientific Reports, vol. 7, no. 1, pp. 1-11, 2017.  https://doi.org/10.1038/s41598-017-03860-0 

[29] D. Khanal, D. B. Thapa, K. H. Dhakal, M. P. Pandey, and B. P. Kandel, "Correlation and path coefficient analysis of elite spring 
wheat lines developed for high temperature tolerance," Environment & Ecosystem Science, vol. 4, no. 2, pp. 56-59, 2020.  
https://doi.org/10.26480/ees.02.2020.73.76 

http://www.fao.org/faostat/en/#data/QC
https://nepalindata.com/resource/statistical-information-nepalese-agriculture-207374-201617/
https://doi.org/10.3126/ijasbt.v9i1.35985
https://doi.org/10.4060/CA1796EN
https://doi.org/10.1371/journal.pone.0236351
https://doi.org/10.56093/ijas.v85i1.45935
https://doi.org/10.3390/su13052972
https://www.noaa.gov/
https://doi.org/10.1017/9781009157896
https://doi.org/10.1111/j.1365-2486.2010.02262.x
https://doi.org/10.1007/s11738-022-03365-y
https://doi.org/10.1016/j.agsy.2020.102826
https://doi.org/10.1515/cerce-2015-0004
https://doi.org/10.26480/ppsc.02.2021.45.49
https://doi.org/10.15835/nsb437879
https://doi.org/10.23910/ijbsm/2016.7.6.1666a
https://doi.org/10.26832/24566632.2020.0502017
https://doi.org/10.26480/bda.01.2022.01.07
https://doi.org/10.1111/jac.12572
https://doi.org/10.1111/jac.12363
https://doi.org/10.1038/s41598-017-03860-0
https://doi.org/10.26480/ees.02.2020.73.76


Agriculture and Food Sciences Research, 2024, 11(1): 30-35 

35 
© 2024 by the authors; licensee Asian Online Journal Publishing Group 

 

 

[30] A. Baye, B. Berihun, M. Bantayehu, and B. Derebe, "Genotypic and phenotypic correlation and path coefficient analysis for yield 
and yield-related traits in advanced bread wheat (Triticum aestivum L.) lines," Cogent Food & Agriculture, vol. 6, no. 1, p. 1752603, 
2020.  https://doi.org/10.1080/23311932.2020.1752603 

[31] V. K. Meena, R. Sharma, S. Yadav, N. Kumar, R. Gajghate, and A. Singh, "Selection parameters for improving grain yield of bread 
wheat under terminal heat stress," Indian Journal of Agricultural Sciences, vol. 91, no. 3, pp. 468-73, 2021.  
https://doi.org/10.56093/ijas.v91i3.112536 

[32] C. E. d. S. Oliveira, A. d. F. Andrade, A. Zoz, R. L. Sobrinho, and T. Zoz, "Genetic divergence and path analysis in wheat cultivars 
under heat stress," Pesquisa Agropecuária Tropical, vol. 50, pp. 1–9, 2020.  https://doi.org/10.1590/1983-40632020v5065493 

[33] M. K. Bundela, S. C. Pant, M. Singh, and K. Singh, "Correlation and path coefficient analysis in bird’s eye chilli (Capsicum 
Frutescens L.,) for yield and yield attributing traits," International Journal of Agricultural Science Research, vol. 7, no. 11, pp. 65–70, 
2018.  https://doi.org/10.24247/ijasrjun201733 

[34] M. G. Usman, M. Y. Rafii, M. Y. Martini, Y. Oladosu, and P. Kashiani, "Genotypic character relationship and phenotypic path 
coefficient analysis in chili pepper genotypes grown under tropical condition," Journal of the Science of Food and Agriculture, vol. 97, 
no. 4, pp. 1164-1171, 2017.  https://doi.org/10.1002/jsfa.7843 

[35] P. Kayastha et al., "Correlation coefficient and path analysis of yield and yield attributing characters of rice (Oryza sativa L.) 
genotypes under reproductive drought stress in the terai region of Nepal," Archives of Agriculture and Environmental Science, vol. 7, 
no. 4, pp. 564-570, 2022.  https://doi.org/10.26832/24566632.2022.0704013 

 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

  

Asian Online Journal Publishing Group is not responsible or answerable for any loss, damage or liability, etc. caused in relation to/arising out of the use of the content. 
Any queries should be directed to the corresponding author of the article. 
 

https://doi.org/10.1080/23311932.2020.1752603
https://doi.org/10.56093/ijas.v91i3.112536
https://doi.org/10.1590/1983-40632020v5065493
https://doi.org/10.24247/ijasrjun201733
https://doi.org/10.1002/jsfa.7843
https://doi.org/10.26832/24566632.2022.0704013

