African Journal of
Biotechnology

  • Abbreviation: Afr. J. Biotechnol.
  • Language: English
  • ISSN: 1684-5315
  • DOI: 10.5897/AJB
  • Start Year: 2002
  • Published Articles: 12487

Full Length Research Paper

Correlation and path coefficient analysis of agronomic and quality traits in a bioenergy crop, sweet sorghum [Sorghum bicolor (L.) Moench]

Chalachew Endalamaw
  • Chalachew Endalamaw
  • Ethiopian Institute of Agricultural Research (EIAR), Melkassa Agriculture Research Center, P. O. Box 436 Adama, Ethiopia.
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Asfaw Adugna
  • Asfaw Adugna
  • Advanta Seeds Ltd., P. O. Box 10438, Eldoret, Kenya.
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Hussein Mohammed
  • Hussein Mohammed
  • Department of Plant and Horticultural Sciences, Hawassa University, P. O. Box 05, Hawassa, Ethiopia.
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  •  Received: 10 September 2017
  •  Accepted: 13 November 2017
  •  Published: 22 November 2017

 ABSTRACT

Sweet sorghum is considered one of the best sources of bioethanol due to its higher total reducing sugar content, which ferments completely to produce ethanol coupled with its adaptation to the changing climate. A study was carried out in Ethiopia, during the 2015/16 crop season, to determine the extent of phenotypic and genotypic relationships among 13 agronomic traits and six quality components of 28 sweet sorghum genotypes. Panicle weight and width, dry matter yield, thousand kernel weight and harvest index had significant positive correlation with grain yield and exerted favourable direct effects both at phenotypic and genotypic levels. Ethanol yield was also correlated with juice yield, sugar yield and fresh stalk yield. Therefore, these yield and quality components are suggested to receive due attention during sweet sorghum varietal selection. Moreover, days to maturity had negative correlation and imposed negative direct effect on grain yield, which may indicate the possibility to select high yielding, early maturing dual purpose varieties for dry environments where terminal drought is rampant. The studied genotypes were grouped into three clusters according to their D2 values, worthy of future breeding work considering the special merits in each cluster depending on the objectives of the breeding program. Some of the genotypes excelled as one of the two commercial sugarcane varieties used as controls for some quality traits. Therefore, considering their less water requirement, faster production cycle, and additional advantage of grain production over sugarcane, sweet sorghum stalks can serve as alternatives to sugarcane for use as feedstock in drier areas of the world under the changing climate.

Key words: Bioethanol, correlation, juice, path-coefficient, sweet sorghum.


 INTRODUCTION

Sorghum’s ability to withstand drought and heat  stresses and to give reasonable yields under adverse environmental conditions have raised its importance as a food security and bioenergy crop in arid and semi-arid tropics. In stress environments, pearl millet and sorghum are the dominant crops and receive fewer agricultural inputs than any other major cereals (McGuire, 2008). Sweet sorghum (Sorghum bicolor) is a natural variant of common grain sorghum with high stem sugar content, which can offer both food and fuel. The sugar content in the stalk juice of sweet sorghum reaches 10 to 25% at grain maturity (Pei et al., 2010). This sugar in the juice can be used to produce table sugar, syrup, wine or biofuel. The bagasse is used as forage or as raw material for the paper industry (Koeppen et al., 2009).
 
Sweet sorghum ensures food and feed security and provides opportunities for additional income for small farmers serving as a feedstock for bioethanol production while protecting the environment (Almodare and Hadi, 2009). It requires 37% less nitrogen fertilizer and 17% less irrigation water than maize, and could yield more ethanol than maize during a dry year (Hills et al., 1990; Putnam et al., 1991). Its potential ethanol yield of 5000 L/ha/yr is more than that of sugarcane, maize, cassava and wood (Hodes, 2006). There is an increasing interest in using sweet sorghum as source of bioethanol due to its various salient features including the higher total reducing sugar (glucose and fructose) and poor sucrose contents compared to sugarcane juice (Huligol et al. 2004), which prevent crystallization resulting in near complete fermentation efficiency to produce ethanol (Ratnavathi et al., 2004; Anderson, 2005).
 
In general, alcohol as a fuel is clean, burning when used alone and when mixed with gasoline it acts to increase the octane rating (Schaffert and Gourley 1982), which may also mean that it contributes to climate change mitigation. Moreover, because of its efficient conversion of atmospheric CO2 into sugar, sweet sorghum is a promising crop for use in the bio-energy industry and the ethanol production process from sweet sorghum is eco-friendly with less or no environmental pollution compared to that from molasses. However, information on the relationship of different agronomic and quality characters directly or indirectly involved in ethanol production is still meager. Therefore, the present study was carried out: to quantify the genetic correlations among various morpho-agronomic and quality traits in sweet sorghum genotypes; to identify the grain yield and bioethanol production potential among the genotypes; and to partition the correlation coefficients of various traits into direct and indirect effects


 MATERIALS AND METHODS

Description of the study site
 
Melkassa Agricultural Research Center (MARC) is located in the central Rift Valley of Ethiopia at a distance of 115 km from the capital Addis Ababa and 16 km south east of Adama town. The site is placed at an altitude of 1500 m above sea level  on  geographical coordinates of 8° 30’ latitude and 39° 21’ longitude. The area receives mean annual rainfall of 763 mm and the mean maximum and minimum temperatures are 24.8 and 14.0°C, respectively. Agro-ecologically, the area is categorized as dry semiarid. The soil is a well-drained typical sandy loam Andosols with a pH of 8.0.
 
Treatments and experimental design
 
The treatments consist of 28 genotypes including 26 accessions of sweet sorghum introduced from ICRISAT and preserved at MARC and two released grain sorghum varieties (Meko and Gambella 1107) as standard check (Appendix Table S1). The experiment was laid out in a Randomized Complete Block Design with three replications with each plot having 4 rows of 5 m length with row spacing of 0.75 m. Sowing was done by hand drilling on 4 July, 2015. Twenty days after planting (DAP), the seedlings were thinned to 0.15 m distance between plants. Phosphorus and nitrogen fertilizers were applied at the recommended rates of 100 and 50 kg-ha-1 in the form of DAP (46%P2O5, 18%N) and Urea (46%N), respectively. The DAP was applied during planting in the seed furrows with all plots top-dressed with urea when the plants reached 30 cm height. The experiment was conducted under rain fed conditions. Moreover, two commercial sugarcane varieties, [NCO-334(Cip) and B52298 (Wonji-1) from Wonji Sugar Corporation Estate of Ethiopia were used for comparison.
 
 
Data recording and analysis
 
Agronomic characteristics
 
Data were recorded on days to 50% flowering (DTF), days to maturity (DTM), plant height (PH) (cm), stalk diameter (cm) (STD), number of productive tillers (NPT), panicle length (PL) and width (PW), panicle weight (PWT), fresh stalk yield (FSY), dry stalk yield (DSY), thousand kernel weight (TKW) (at 12% moisture) and grain yield (GY). Stalk diameter was recorded as the average width of the middle part of the stem from five randomly selected plants in a plot at maturity using vernier caliper and record in centimeters. Productive tillers were recorded as the number of tillers that bear grain recorded from five randomly taken plants at maturity. Fresh stalk yield (kg) was measured from the two central rows of 25 randomly selected plants in each plot before harvesting for juice extraction. Dry stalk yield (kg) was measured from the two central rows of five randomly selected plants in each plot after.
 
Quality characteristics
 
The juice was extracted from 25 plants randomly taken from the two central rows in each plot and the volume was measured at hard dough stage. The juice was extracted using roller mills at Wonji Sugar Corporation Estate of Ethiopia. Due to the relatively long distance from the trial site to the extracting machine, the sample juice volume was expected to be biased. Therefore, alternative method of Wortmann et al. (2010) was followed to adjust the lost juice and sugar yields as follows:
 
JY (80% extracted) = [FSY – (DSY – CSY)] × 0.8;
 
CSY = (FSY – DSY) × Brix × 0.75;
 
SY=JY × Brix × 0.75;
 
Where, JY = juice yield (t ha-1), FSY = fresh stalk yield (t ha-1); DSY = dry stalk yield (t ha-1); CSY = conservative sugar yield (t ha-1) and SY = sugar yield (t ha-1).
 
Brix % (BRX) was measured in the field from five randomly selected plants at middle portions of the stem from two central rows using refractometer (Atago 2522; Atago USA Inc., Bellevue, WA). To measure pol percent (POL), 200 ml of juice was transferred into a 300-ml Erlenmeyer flask, after purification with dry lead (Hornes dry lead) through filter paper No. 42. The pol tube was filled with the filtrate juice, and the POL reading was recorded from the Saccharimeter. Purity percent of the juice (PTY) was computed as (POL/BRX)100. Sugar yield estimates were calculated following the approach of Wortmann et al. (2010) that assumes 75% of the BRX as fermentable sugars. Theoretical EY (L·ha−1) was calculated from extracted juice as SY (kg·ha−1) multiplied by a conversion factor (0.581 L kg-1 ethanol) (Teetor et al., 2011). The above ground parts of five plants were chopped and kept in an oven at 70°C for 72 h to get dry stalk yield (DSY). Moreover, harvest index (HI) was calculated using the formula of Donald (1968) and expressed as percent. Ten full canes were randomly collected from each of the two commercial sugarcane varieties from two rows. The plants were 12 months old during the time of sampling and their FSY, JY, BRX, POL, PTY, SY and EY were recorded to compare with the sweet sorghums.
 
Statistical analysis
 
The recorded data were subjected to analysis of variance using the procedures outlined by Gomez and Gomez (1984) using the GLM and PROC MIXED procedures implemented in SAS software v. 9.1.3 (SAS Institute 2003) and Program Genes (Cruz, 2006). Correlations among each pair of characters were also computed. The  paired  D-square  value  was  computed  based  on  the  pooled  mean  of  the  genotypes and cluster analysis was obtained following the techniques of Tocher’s (Rao, 1952).


 RESULTS

Phenotypic and genotypic correlations among agronomic characters

Analysis of variance revealed highly significant differences among the genotypes for all the measured agronomic characters, except for number of tillers, indicating the existence of considerable genetic variability (data not shown). The comparative performance of sweet sorghum genotypes for agronomic traits is presented in Table 1. Phenotypic and genotypic correlations among agronomic characters are also presented in Appendix Table S2. Panicle weight, PW, HI, TKW and DMY were observed to have positive and significant correlations with GY at phenotypic and genetic levels, showing the inter-relationship of these traits.

 

 

This was further confirmed by Path coefficient analysis (Appendix Tables S3 and S4). On the other hand, GY had significant negative correlation with DTM. Dry matter yield had significant positive correlation with DTF, PH, DSY and GY. Panicle weight and DMY had the highest positive direct effect on GY at phenotypic level, but only PWT and PL had the same effect at the genetic level. Days to maturity had negative direct effect on GY at both levels and their indirect effect via other characters was also mostly negative; thus, the relationship was mainly due to both direct and indirect effects.

 

 

Correlations among quality characters

Analysis of variance also revealed highly significant differences among the genotypes for all the measured quality characters (data not shown). The comparative performance of the studied genotypes for quality traits are presented in Appendix Table S5. Phenotypic and genotypic correlations among the quality characters are presented in Table 2. Juice yield and SY were observed to have positive significant (p<0.01) correlations with EY at phenotypic and genotypic levels. Ethanol yield had positive correlation with JY and FSY, but its correlation with the rest of the characters was not significant. Moreover, JY was highly correlated with SY. Plants with greater FSY and JY also produced greater SY and EY. Accordingly, genotypes with higher FSW produced higher JY that can be immediately fermented to bioethanol. Sweet sorghum genotypes those had the highest SY and EY were due to increased juice and high and moderate BRX, but they had moderate GY. Genotypes with high and moderate BRX, and high JY produced high SY and EY, and moderate GY. Brix percentage was significantly (p<0.01) correlated with POL at both levels, but it had no significant associations with other characters. The phenotypic and genotypic direct and indirect effects of different characters on EY are presented in Appendix Tables S6 and S7, respectively. Six of the nine characters studied showed positive direct effects on EY, whereas juice yield had the highest direct effect on EY followed by BRX.

 

 

 

Correlation among agronomic and quality characters

Wide genetic variability was found among the 28 genotypes for FSY, BRX, JY, SY and EY. Also, there were significant positive correlations among EY, FSY, and JY at phenotypic and genotypic levels, but the direct effect of FSY on EY was negligible. Phenotypic and genotypic path analysis showed that DTF, PH, BRX and POL had positive direct effect, but DTM, SW and DSY had negative direct effect and phenotypic and genotypic correlation with EY. Because their indirect effect via other characters was negligible, their phenotypic and genotypic correlation with EY was mainly due to direct effect.

Genetic divergence and cluster mean analysis

D-square analyses grouped the genotypes into three major clusters (Table 3), which may indicate that the tested genotypes were moderately divergent. The largest cluster (Cluster II) comprised of 16 genotypes (57.14%). Eight genotypes were grouped in Cluster III (28.57%) and the remaining four genotypes were included in Cluster I (12.29%). Cluster I was characterized by the highest PH, STD, DSY, DMY, POL, BRX and PTY, whereas Cluster II was characterized by the highest PWT,  GY  and  HI.  On the other hand, Cluster III was characterized by the highest JY, SY, and EY (Appendix Tables S6 and S7). 

 


 DISCUSSION

Phenotypic and genotypic correlations among agronomic characters

Similar to the present study, Tesso et al. (2011) reported that GY was positively associated with TKW, PW and PW among 200 sorghum accessions included in their studies. In the present study, GY had negative correlation with DTM at phenotypic and genotypic levels, which was in agreement with the results of Patted et al. (2011). Furthermore, DTM had negative direct effect on GY at both levels, and their effect via other characters was also mostly negative. These negative correlations may help to select early maturing genotypes with high grain yield for moisture stressed areas where terminal drought is recurrent. Moreover DTF was negatively correlated with such characters as PL, TKW and HI, which was similar to the results of Gaikwad et al. (2013) and Sowmy et al. (2015). Tesso et al. (2011) also reported significant negative correlation between TKW and DTF.

Thousand kernel weight was positively correlated with PW and HI. Panicle width and PL had the highest positive direct effect on GY at genetic level, which shows that the correlation explained the true relationship and suggests that direct selection for these traits could be effective. Meanwhile, similar results were previously reported by Sowmy et al. (2015). The phenotypic and genetic residual value (0.218 and 0.04, respectively) showed that the characters in the path coefficient analysis accounted for 78.2 and 96% of the variation in GY at phenotypic and genetic levels, respectively (Appendix Tables S3 and S4). The positive associations among GY with PW and PW, TKW and DMY indicate that selecting for positively associated panicle related traits would have a positive effect on GY. Negative correlations were observed among some traits which could be utilized in breeding for negatively correlated traits.

Correlation among quality characters

The significant (p<0.01) correlations of JY and SY with EY in this study was in agreement with the results of Makanda et al. (2009) and Rutto et al. (2013) and may indicate the usefulness of these characters to improve EY. Thus, breeding for higher juice type genotypes might result in higher SY and EY than other traits. Brix was found to have no direct contribution to EY, which was against Gaikwad et al. (2013). Generally, correlation analyses indicated greater contribution of JY to higher SY and EY than BRX alone suggesting that improvement for high SY and EY could be achieved through selecting genotypes with high JY.

Given the same BRX value, genotypes with greater JY produced higher sugar and ethanol yields (Table 2). Similar to this result, Makanda et al. (2009) reported that genotypes with higher JY and lower BRX had better SY than those genotypes with lower JY and higher BRX. The highest performing genotypes in the present study also confirmed that JY is an important trait for selection of higher SY and EY. Juice yield, which had positive and highly significant phenotypic correlation (r = 0.970**) with EY had also the highest direct effect  at  phenotypic  and  genotypic  levels,  which was in agreement with the results of Shinde et al. (2013).

Correlation among agronomic and quality characters

The positive correlation between EY and FSY, and EY and JY at phenotypic and genotypic levels may indicate that sweet sorghum genotypes with improved SY and EY can be utilized for genetic improvement. The correlation between EY and FSY was in agreement with the findings of Alhajturki et al. (2014). Prasad et al. (2013) also reported significant correlations among EY, FSY, SY and JY. The positive correlation (r = 0.35*) of FSY with EY but negligible direct effect at both levels, may indicate that high FSY with high JY is a pre-requisite for high ethanol recovery (Rani and Umakanth, 2012). Hence, these traits could be utilized in the sweet sorghum breeding program. Critical analysis of character association and path analysis suggests that more focus needs to be given in selection programs for traits such as BRX, FSY, and JY.

Phenotypic and genotypic path analysis showed that DTF, PH, BRX and POL had positive direct effect and phenotypic and genotypic correlation, but DTM, SW and DSY had negative values with EY. Because their indirect effect via other characters was negligible, their phenotypic and genotypic correlation with EY was mainly due to direct effect. The JY could also be directly related to FSY. Previous studies in sweet sorghum showed that FSY was correlated with PH, SD and JY (Audilakshmi et al., 2010) which it entails. Therefore, selection for FSY needs to take into consideration PH, SD, BRX and JY. Significant genotypic variability among sweet sorghum germplasm was also reported by Ali et al. (2008) and Murray et al. (2009) for PH and juice BRX.

D-square and cluster mean analyses

It is essential to determine how the influential traits lead to an improved sweet sorghum cultivar. The present study showed significant variation among the genotypes for the traits considered. Improvement in EY and GY could be achieved by direct or indirect selection for high yielding genotypes and for yield components positively associated with these target traits. Genotypes were grouped into three clusters and the future breeding program utilizing the studied accessions is suggested to be based on the genetic analysis of the various traits to which clusters are predominant. Hence, for future breeding work it could be useful to select individual genotypes from these clusters by considering the special advantages of each cluster and the objectives of the breeding program.

Advantages of sweet sorghum over commercial sugarcane as sources of bioethanol

Among the 28 genotypes, seven (NTJ 2, SDSL 90167, 104GRD, E36-1, Ent.#64DTN, IESV 92028 DL and S 35) had sugar-rich juice (JY, SY and EY), comparable to one of the commercial sugarcane varieties, NCO334(Cip) but the other commercial sugarcane variety B52298(Wonji-1) was superior in all characters (Appendix Table S8). Sweet sorghum genotypes were harvested in less than four months of growth period at MARC, while sugarcane varieties were 12 months old at the time of sample collection. Sweet sorghum genotypes were grown with less rainfall, whereas sugarcane varieties used all available rain and a large amount of irrigation water. 

 

 

Previous reports by Soltani and Almodares (1994) showed that sweet sorghum grown for ethanol production in India took about four months and water requirement of 8000 m3 over two cropping seasons, which was four times less than those of sugarcane (12 to 16 months and 36,000 m3 crop-1, respectively). Similarly, it has been shown that the cost of cultivation of sweet sorghum is three times less than that of sugarcane (Dayakar Rao et al., 2004). In this study, sugarcane varieties had higher pol (sucrose), PTY, FSY and BRX than sweet sorghum genotypes (Appendix Table S9). However, in addition to sweet-stalk, which can be sold out to the distillers, grain yield of 4.22 to 8.9 t ha-1 is an added advantage of sweet sorghum over sugarcane, which can be used as food or for sale by the small holder farmer.

 


 CONCLUSION

Those yield and quality components, which were significantly correlated in these study are suggested to receive due attention during sweet sorghum varietal selection. The study has also shown the possibility of selecting high yielding, early maturing varieties for dry environments where terminal drought is rampant. Considering their less water requirement, faster production cycle, and additional advantage of grain production over sugarcane, sweet sorghums can serve as very good alternatives to sugarcane for use as feedstock to ethanol distillers in the drier areas of the world under the changing climate.


 CONFLICT OF INTERESTS

The authors have not declared any conflict of interests.


 ACKNOWLEDGEMENTS

The authors thank the national sorghum coordination program at Melkassa Agricultural Research Center for providing with the seed of the sweet sorghum. The support in juice extraction and provision of test sugarcane by Wonji Sugar Estate is duly acknowledged.



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