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Research Article | Volume 2 Issue 2 (July-Dec, 2021) | Pages 1 - 5
Simple Correlation Coefficients for Yield, Yield Components and Other Growth Attributes in Some Wheat (Triticum Aestivum L.) Genotypes
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1
Part of PhD thesis submitted by the author to the Faculty of Agricultural and Natural Resources, University of Kassala, 31112 Sudan
2
Department of Crop Sciences, Faculty of Agricultural and Environmental Sciences, University of Gadaref, 32215 Sudan
3
Department of Crop Science, Faculty of Agriculture and Natural Resources, University of Kassala, 31112 Sudan
Under a Creative Commons license
Open Access
Received
Oct. 10, 2021
Revised
Oct. 25, 2021
Accepted
Nov. 15, 2021
Published
Dec. 31, 2021
Abstract

Two wheat (Triticum aestivum L.) genotypes were evaluated over two seasons (2017/2018 and 2018/2019), at three locations in the irrigated scheme of Newhalfa, Sudan, to study the inter-relationships between yield, yield components and other agronomic traits of the genotypes. A randomized complete block design with three replications was used. Data were collected on grain yield, some of its components and other growth attributes, simple correlation coefficients among these traits were calculated. The results attained showed that, at most of the testing sites, number of grains per plant, number of spike lets per plant and spike length were significantly positively correlated with grain yield of the crop, and among each other’s. On the other hand, the growth attributes with exception of the plant height were significantly positively correlated with each other and negatively correlated with yield and its components at most of the testing environments. Over all, plant height, number of tillers per plant number of spike lets per spike, number of grain per plant, spike length and 1000-grain weight had high positive correlation with yield, thus direct selection for these characters, would improve grain yield of the crop.

Keywords
Important Note

Key findings:

Two wheat genotypes were studied over two seasons in Sudan. Positive correlations were found between grain yield and traits such as number of grains per plant, spikelet number, and spike length. Plant height, tillers per plant, and 1000-grain weight also correlated positively with yield, suggesting they could be targeted for improved crop yield.

 

What is known and what is new?

Existing knowledge suggests that wheat yield is influenced by various agronomic traits, including spikelet number, spike length, and plant height. This study adds to this by specifically evaluating wheat genotypes in Sudan's Newhalfa region, identifying correlations between these traits and grain yield. The finding that certain traits like plant height and spikelet number correlate positively with yield provides new insights for targeted breeding and management practices in the region.

 

What is the implication, and what should change now?

The implication of this study is that breeding programs and agronomic practices in the Newhalfa region should focus on traits such as plant height, spikelet number, and spike length to improve wheat yield. Farmers and researchers should prioritize these traits in their selection and management strategies to enhance wheat productivity in Sudan. Additionally, further research could investigate the specific genetic factors underlying these traits to facilitate targeted breeding efforts.

Introduction

Worldwide, wheat (Triticum aestivum L.) importance as a food crop is well known. To meet the requirements of the consistently increasing world population (around 7 billion). Rosegrant and Agcaoili (2010) [1] observed that the increasing demand on the wheat consumption by increasing world population must be met by an annual increase in wheat production of 2% till 2050. In Sudan wheat, is important stable food for a large number of citizens, especially in urban areas, Being a temperate cool crop, it is not indigenous to Sudan; yet it was traditionally grown since early times in the Northern states (Latitude. 18º -22´ N) that are characterized by a relatively cooler and longer winter season than the center [2]. As yield is a complex and polygenic, and highly affected by other genetic factors and environmental variations. Moreover, plant breeder added that, direct selection for yield as such is unpredictable, thus successful selection needs an information on the genetic variability and association of agronomic characters with grain yield.

 

Associations among the characters provide a better understanding of the inter- relation of different traits with grain yield [3]. Understanding of the inter-relation between yield, yield components and other growth attributes in wheat is very important, because selection in one character may improve or deteriorate associated characters, depending on the nature and magnitude of the association among them. Genetic correlations are the most important, and are caused by linkage or pleiotropy or both [4,5]. Adam 1967 [6], however, believed that correlations may be developmental rather than genetic per se and are postulated to be due to genetically independent components, developing in sequential patterns. Grain yield and its quality are the principal characters of a cereal crop [7]. They are complex quantitative characters, which are affected by numerous yield related traits. Therefore, the selection for desirable genotypes should not only be based on yield per se, and the other yield components should also be undertaken. Direct selection for yield is often unpredictable in wheat because the crop yield is a complex trait. 

 

For effective using of the genetic pool in crop improvement, information of mutual inter-relationship between yield and yield components is quite important. It is therefore, necessary to know the association of different component traits with yield and among each other’s. The correlation coefficients among yield and yield related traits usually show a complex chain of interacting relationship. The objective of this study was to find out simple correlations between yield, yield components including Spike length cm (SL), Number of Spikelet/Plant, Number of Grains/Plant, Grain yield/Plant g, 1000 Grain weight g, and other growth attributes and to determine the most important characters can be used as selection criteria for improving grain yield in wheat.

Materials & Methods

The experiment was carried out for two consecutive seasons(2017/2018 and 2018/2019), at three sites of New Halfa irrigated scheme, eastern Sudan, namely, Hajer (HA) represents South of the scheme, Faculty of Agriculture and Natural Resources (FA) of the University of Kassala, represents central area of the scheme and Shebiake (SH) which represent North part of the scheme. (Latitude 15º 19´ N, Longitude 35 º 36´ E. and Altitude 450m asl.), (see Fig. 1). The soil is calcareous, alkaline (PH= 8.3), nonsaline, nonsodic and moderately fertile. It is classified as Typic Haplusterts, very fne, semectic, isohyperthermic [8]. Two wheat cultivars seeds (Bohain and Debiara) were offered from the Agricultural Research Corporation (ARC) of New Halfa. At each location, a randomized complete block design with three replicates was used. The plot size was 4.5 x 6 m, sowing date was on 12th and 14th November, for the first and the second seasons, respectively. All cultural practices were followed as recommended from ARC. Data were collected on the following parameters: plant height(cm), (PH), Number of tillers/plant (NTPP), Number of leaves/plant (NLPP), Leaf Area cm2 (LA), Spike length cm (SL), Number of Spikelet/Plant (NSPP), Number of Grains/Plant(NGPP), Grain yield/Plant g (GYPP), 1000 grain weight g (1000 GW), All the data were analyzed according to the procedure described by Gomez and Gomez (1984) [9] using statistix computer software package (version 9-1) to calculate simple Spearman correlation coefficients among the different pairs of the nine characters for each genotype.

 

Fig. 1: New Halfa Irrigated Scheme Map

Results and Discussion

Tables 1, 2 and 3 show simple correlation coefficients between yield, yield components and the growth attributes for the two genotypes of wheat grown at the three sites of New halfa irrigated scheme, Hajer, Faculty of Agriculture and Natural resources and Shebaik for the two consecutive seasons 2017/2018 and 2018/2019. Plant height, showed positive and significant correlation at the three sites of the study and in both seasons with leaf area and spike length and it showed positive significant correlation with number of leaves per plant at the three sites and in both seasons, except the Faculty site in the first season, Shebaik in the first and second seasons. However, plant height was significantly negatively correlated with 1000 Grain weight at the three sites and over the two seasons, and significantly negatively correlated with grain yield per plant at the three sites and for the two seasons except, at the faculty site in both seasons and Shebaik site in the second season. At the three sites and over the two seasons, number of tillers per plant was highly significantly positively correlated with the number of leaves per plant and with leaf areas except at Hajer in the first season and Shebaik site in the second season. Number of leaves per plant was highly significantly and positively correlated with the leaf areas at the three sites and over the two seasons. The spike length showed positive significant correlation with number of grains per plant at all sites, except at the Faculty and Shebaik in the first season and Hajer in the second season. The number of spike lets per plant and number of grains per plant were highly and significantly positively correlated with grain yield per plant and with each others. 1000-grain weight and grain yield per plant were significantly positively correlated almost at the three sites of the study. These results were in accordance with that reported by Kumar et al., (1998); Narwal et al., (1999); Ashok Kumar et al., (2000); Subhani (2000); Esmail (2003); Singh et al., (2006), Saktipada et al., (2008) and Bilgi (2006) [10-18]. In this study, most of the yield components and some other essential growth attributes were highly positively associated with yield of wheat almost at the three sites of the study and over the two seasons and hence direct selection for these traits could improve the yield of the crop. [16] attributed the genetic correlation to peilotropy or linkage or both. In peilotropy, the same gene, the same gene determines the expression of two or more characters, and the correlation here is unbreakable. However the correlation arising from linkage is transient and can be manipulated. Knowledge of associations is important in assessment of changes brought about by selection that simultaneously that influence other correlated traits. In general, plant height, number of tillers per plant, number of spikelets per spike, number of grain per plant, spike length and 1000-grain weight are the most important for grain yield since these traits had high correlation, Therefore, direct selection for these characters should be of concern to the breeders, for improving the yield of the crop.

 

Table 1: Simple Correlation Coefficients between Growth Attributes, Yield and Yield Components in Two Wheat Genotypes at Hajer Site in Season 2017/2018(Upper Diagonal) and 2018/2019 (Lower Diagonal).

CharacterPHNTPPNLPPLASLNSPPNGPPGYPP1000 GW
PH-0.1126ns0.5971***0.5447***0.7066***-0.0162ns0.5991***-0.2507**-0.6902***
NTPP0.1951*-0.3347***0.1499 ns0.0694 ns-0.0438 ns0.0623 ns0.0969 ns-0.1184 ns
NLPP0.2434*0.9186***_0.6761***0.4186***0.0225ns0.4136***-0.0320 ns-0.4547***
LA0.4962***0.5199***0.6653***-0.5535***-0.0850ns0.4190***-0.1294ns-0.5180***
SL0.2650**0.1376 ns0.1364 ns0.4040***_--0.2219*0.7247***-0.1282 ns-0.8774***
NSPP -0.0315 ns-0.1337 ns.-0.1331 ns-0.2008*0.1551 ns-0.21120.3500***0.1361 ns
NGPP-0.2592**-01290ns-0.2055*-0.4262***-0.1162 ns0.4996***-0.1216 ns-0.6987***
GYPP -0.3320***-0.0599 ns-0.1452 ns-0.4846***-0.0005 ns0.4595***0.5223***-0.2408*
1000 GW-0.3902***-0.0153 ns-0.0752 ns-0.3433***-0.4489***-0.0288ns0.1198 ns0.3097**-

*, **, *** Significant at 0.05, 0.01 and 0.001 Probability, Respectively

ns; not Significant

 

Table 2: Simple Correlation Coefficients between Growth Attributes, Yield and Yield Components in Two Wheat Genotypes at Faculty of Agriculture Site in Season 2017/2018(Upper Diagonal) and 2018/2019 (Lower Diagonal).

CharacterPHNTPPNLPPLAS LNSPPNGPPGYPP1000GW
PH_0.0008ns0.0301ns0.1972*0.0266 ns0.0124 ns-0.1066 ns-0.0527 ns0.0244 ns
NTPP0.1710 ns_0.7503***0.3737***-0.1714 ns0.1419 ns0.1709 ns0.1898*-0.1396 ns
NLPP0.2230*0.8433***_0.7355***0.3123**-0.1153 ns-0.2278*-0.2404*0.0288 ns
LA0.3120**0.6098***0.6846***_0.5475***-0.3103**-0.4700***-0.4489***0.2301*
SL0.3831***0.1673 ns0.2090*0.1367 ns_-0.3796***-0.6102***-0.5498***0.2891**
NSPP0.1780 ns0.0356 ns-0.0137 ns-0.0114 ns0.1597 ns_0.7418***0.5468***-0.6054***
NGPP0.1887*-0.0800 ns-0.0275 ns-0.0474 ns0.5103***0.3999***_0.6750***-0.6666***
GYPP0.2150*0.1535 ns0.1486 ns0.2539**0.3159***0.2642**0.4754***_-0.3734***
1000 GW0.1706 ns0.1134 ns0.0936 ns-0.0211 ns0.1081 ns0.0897 ns0.0406 ns0.2317*_

*, **, *** Significant at 0.05, 0.01 and 0.001 Probability, Respectively

ns; not Significant

 

Table 3: Simple Correlation Coefficients between Growth Attributes, Yield and Yield Components in Two Wheat Genotypes at Elshebaik Site in Season 2017/2018(Upper Diagonal) and 2018/2019 (Lower Diagonal).

CharacterPHNTPPNLPPLASLNSPPNGPPGYPP1000 GW.
PH_-0.3856***-0.1833*0.5786***0.5883***-0.1233 ns0.0753 ns-0.1968*-0.2375*
NTPP-0.0237 ns_0.9263***0.1928*-0.2264*0.2569**0.0397 ns0.2124*0.0888 ns
NLPP-0.0293 ns0.8328***_0.3509***-0.0880 ns0.2106*0.1611 ns0.1736 ns0.0274 ns
LA0.2482**0.1131 ns0.1440 ns_0.5642***0.0428 ns0.0723 ns-0.1296 ns-0.2381*
SL0.4635***-0.2100*-0.2825**0.3434***_-0.0366 ns0.1338 ns0.1347 ns-0.3836***
NSPP0.4204***-0.1674 ns-0.1588 ns0.2558**0.8384***_0.0998 ns0.3833***0.0506 ns
NGPP0.4698***-0.1770 ns-0.2429*0.3586***0.7356***0.6007***_0.3021**-0.2244*
GYPP0.3725***-0.1857*-0.2308*0.3012**0.7978***0.7269***0.7165***_-0.1400 ns
1000 GW-0.2142*0.1034 ns0.2055*-0.1771 ns-0.4249***-0.1854*-0.3236***-0.1810 ns_

*, **, *** Significant at 0.05, 0.01 and 0.001 Probability, Respectively

ns; not Significant

 

Funding: No funding sources 

 

Conflict of interest: None declared

 

Ethical approval: The study was approved by the Institutional Ethics Committee of University of Kassala

References
  1. Rosegrant, M. W., and M. Agcaoili. "Global Food Demand, Supply and Price Prospects to International Food Policy, Research Institute, Washington, DC." DC. USA (2010).

  2. Mohammed, Maarouf I. "Genotype x environment interaction in bread wheat in Northern Sudan using AMMI analysis." American-Eurasian Journal of Agriculture & Environment Science 6.4 (2009): 427-433. 

  3. Dixit, P., and D. K. Dubey. "Path analysis in lentil (Lens culinaris Med.)." (1984). https://agris.fao.org/search/en/providers/122511/records/64774eaabc45d9ecdbc03f7e

  4. Miller, P. A., et al. "Estimates of genotypic and environmental variances and covariances in upland cotton and their implications in selection 1." Agronomy journal 50.3 (1958): 126-131. https://doi.org/10.2134/agronj1958.00021962005000030004x

  5. Miller, P. A., and J. O. Rawlings. "Selection for increased lint yield and correlated responses in upland cotton, Gossypium hirsutum L. 1." Crop Science 7.6 (1967): 637-640. https://doi.org/10.2135/cropsci1967.0011183X000700060024x

  6. Adam, M. W. (1976). Basis of yield components compensation in Crop Plant Sciences, 7, 505-510.

  7. Xie, Quan. Physiological and genetic determination of yield and yield components in a bread wheat× spelt mapping population. Diss. University of Nottingham, 2015. http://eprints.nottingham.ac.uk/28998/

  8. Gomez, Kwanchai A., and Arturo A. Gomez. Statistical procedures for agricultural research. John wiley & sons, 1984. 

  9. Soil Survey Staff, (1999). Soil Taxonomy. A basic system of soil classification for making and interpreting soil survey. USDA Handbook No. 436

  10. Kumar, A. S. H. O. K., D. P. Singh, and S. K. Yadav. "Partitioning of dry matter accumulation in aestivum and durum wheats under irrigated and non-irrigated field conditions." (1987): 126-129. https://www.cabidigitallibrary.org/doi/full/10.5555/19880707720

  11. Narwal, N. K., P. K. Verma, and M. S. Narwal. "Genetic variability, correlation and path-coefficient analysis in bread wheat in two climatic zones of Haryana." (1999): 73-76. https://www.cabidigitallibrary.org/doi/full/10.5555/20001606423

  12. Ashok, Kumar, R. S. Verma, and S. B. Samdarshi. "Correlation studies on yield and yield attributes of wheat (Triticum aestivum)." Gujarat Agricultural University Research Journal 25.2 (2000): 5-8. https://www.cabidigitallibrary.org/doi/full/10.5555/20023111944

  13. Subhani, Ghulam Mahboob, and Muhammad Aslam Chowdhry. "Correlation and path coefficient analysis in bread wheat under drought stress and normal conditions." Pakistan Journal of Biological Sciences 3.1 (2000): 72-77. https://www.academia.edu/download/86465586/qredirect.pdf

  14. Esmail, R. M. "Correlation and path coefficient analysis of some quantitative traits with grain yield in bread wheat (Triticum aestivum L.)." (2001): 395-408. https://www.cabidigitallibrary.org/doi/full/10.5555/20023071319

  15. Singh, K. N., et al. "Genetic variability of wheat (Triticum aestivum) lines for grain yield and component characters grown under sodic and waterlogged conditions." (2006): 414-419. https://www.cabidigitallibrary.org/doi/full/10.5555/20063174889

  16. Saktipada Mandal, Saktipada Mandal, et al. "Character association for grain yield components in wheat (Triticum aestivum L.)." (2008): 145-147. https://www.cabidigitallibrary.org/doi/full/10.5555/20083115194

  17. Bilgi, Sarfaraz A. Physiological investigations in Dicoccum Wheat genotypes. Diss. UAS, Dharwad, 2006. URI: http://krishikosh.egranth.ac.in/handle/1/80794

  18. KS, Gill. "Pearl millet and its improvement." Publications and Information Division, ICAR, New Delhi (1991): 1-7. 

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