Characterisation of sweet stem sorghum genotypes for bio-ethanol production

ABSTRACT

In an effort to characterise and select promising sweet stem sorghum genotypes with enhanced biofuel productivity, the present study investigated phenotypic variability present among diverse sweet stem sorghum genotypes based on ethanol production and related agronomic traits. One hundred and ninety genotypes were evaluated. Data were subjected to variance, cluster, correlation, path coefficient and principal component analyses. Significant differences (P < 0.01) were detected among tested genotypes for all measured traits. Days to flowering varied from 62 to 152 with a mean of 93. Plant height varied from 90 to 420 cm with a mean of 236 cm. Stem diameter ranged from 7 to 31 mm with a mean of 16 mm. Biomass yield varied from 6.668 to 111.2 t ha⁻¹ with a mean of 30 t ha⁻¹. Stalk dry matter content ranged from 17.2% to 44.2% with a mean of 29.8%, while fibre content varied from 8.92% to 34.8% with a mean of 17.2%. The stalk brix yield varied from 3.3% to 18.9% with a mean of 12.1%. Ethanol productivity ranged from 240.9 to 5500 l ha⁻¹ with a mean of 1886 l ha⁻¹. The best ethanol producing genotypes were AS203, AS391, AS205, AS251 and AS448. Days to flowering, plant height, stalk brix and stem diameter exerted the greatest indirect effects on ethanol production through higher biomass production. Biomass yield had the greatest direct effect on ethanol production. Therefore, the above traits should be considered during breeding sorghum for bio-ethanol production. Also, the traits had high heritability values, hence selection should provide for good genetic gains. Overall, the above sweet stem sorghum genotypes are useful genetic resources for breeding of sorghum with enhanced bio-ethanol production.

KEYWORDS:

• Correlation
• genetic advance
• genetic coefficient of variation
• heritability
• morphological diversity
• path coefficient analysis
• phenotypic coefficient of variation
• principal component analysis

Introduction

Sweet stem sorghum (Sorghum bicolor) possesses a number of adaptive and constituent traits rendering it suitabile as a biofuel crop. In comparison with the other biofuel crops such as sugarcane and sugar beet, sweet stem sorghum exhibits more valuable traits such as lower water and fertiliser requirements, greater drought tolerance and salt tolerance with greater adaptability to tropical, subtropical and temperate climates. The crop has a short harvesting period that lies in the intermittent sugar-harvesting period allowing for crop rotation (Ratnavathi et al. 2011; Eggleston et al. 2013; Morrissey and Thoma 2017). Several recent studies (Vermerris 2011; Calvino and Messing 2012; Mullet et al. 2014; Prakasham et al. 2014; Anami et al. 2015a, 2015b) have made the case for using sweet stem sorghum as a bioenergy crop.

Genetic diversity is a vital prerequisite for selecting suitable parents for creating genetic diversity or for developing hybrid cultivars. Genetic enhancement of crops through conventional breeding is viable if substantial genetic variation is present and the desirable traits are heritable. Development of sweet stem sorghum cultivars for bioenergy requires efficient identification and incorporation of suitable traits from complementary genotypes. A number of morphological traits such as thick stems, tall stalks, late maturity and high biomass are regarded as important attributes of the crop for increased bioethanol production (Elangovan et al. 2014; Lekgari and Dweikat 2014; Regassa and Wortmann 2014; Tesfamichael et al. 2015). For example, Mathur et al. (2017) in their review on the potential of sweet stem sorghum as a biofuel crop indicated the high potential of the crop for biofuel production attributable to increased biomass yields, thicker and fleshier stems, with high juice yields reaching up to 78% of the total biomass, and Brix content ranging from 14% to 23% (Tesfamichael et al. 2015). Breeding for adaptation to tropical and temperate climates, and gene exchange among the five races of sorghum, have endowed sweet stem sorghum with a high level of genetic variability (Elangovan et al. 2014).

Given that most breeding endeavours are time consuming and cost intensive, it is necessary to undertake simultaneous selection of several traits and to investigate the inter-relationships of phenotypic traits. Understanding of the associations among traits will subsequently reduce time and funds invested in breeding programmes (Mangena 2014; Lombardi et al. 2015) for the reason that concurrent enhancement of several traits can be pursued if they are positively correlated. Studies on the correlation of traits are instrumental for assessing the feasibility of mutual selection of two or more traits, based on calculating the influence of selection for secondary traits on genetic gain for the primary trait under consideration. In contrast, if characters are negatively associated, then it is difficult to implement concurrent selection (El Naim et al. 2012). Path coefficient analysis, originally proposed by Wright (1921) and later illustrated by Dewey and Lu (1959), permits the apportioning of correlation coefficients into direct and indirect effects of traits on a dependent variable and accordingly assists in evaluating the cause–effect relationship for effective selection (Ezeaku and Mohammed 2006; Ali, Abbas et al. 2011). Path coefficient analysis has not been not widely applied in sweet stem sorghum selection programmes (Kumar et al. 2012). Its application has gained momentum recently (Tesfaye et al. 2014; Kassahun et al. 2015) mainly due to the upsurge in research on sweet stem sorghum as a biofuel crop.

Phenotypic variability is a reflection of both genetic and non-genetic components (Sami et al. 2013; Yaqoob et al. 2015), hence, approximation of genetic parameters in the context of trait characterisation is an indispensable component for prospective sweet stem improvement programmes. To initiate a successful breeding programme, it is of paramount significance to have a clear comprehension of variability using parameters like genetic co-efficient of variation, heritability and genetic advance (Govindaraj et al. 2010, 2011; Kassahun et al. 2015). Genetic coefficient of variation concurrently with heritability values give the best estimates of the extent of response anticipated from selection (Akhtar et al. 2007).

The African Centre for Crop Improvement (ACCI) of University of KwaZulu-Natal (UKZN) is actively engaged in breeding sweet stem sorghum for bio-fuel production in South Africa. The centre assembled a collection of over 190 sweet stem sorghum genotypes from various countries. The objective of the present study was to determine the phenotypic variability present among diverse sweet stem sorghum genotypes based on ethanol production and seven related agronomic traits. Information presented in the study may assist in selecting promising sweet stem sorghum genotypes with enhanced biofuel productivity for direct production or for future breeding programmes.

Materials and methods

Plant materials and experimental design

The study used 190 sweet stem sorghum genotypes. The genotypes were sourced from various origins including from Zimbabwe, South Africa, the Netherlands, Australia, India, Ethiopia, Mozambique, Kenya, United States of America, Sudan, Zambia, Malawi and Mexico. The South African genotypes were collected from the following provinces: KwaZulu-Natal, Eastern Cape and Limpopo. The test genotypes included Urja and Sugargraze that were used as standard checks. Urja is a commercial sweet stem sorghum hybrid being commercialised by Praj Industries in India. Sugargraze also referred to as SS120 is a three-way hybrid developed in Australia. Detailed information about each genotype is given in Table 1. Experiments were laid out in a lattice design containing 22 incomplete blocks with two complete blocks. Nine genotypes were allocated in each incomplete block. Each entry was planted in two-row plots of 3.0 m length with inter-row and intra-row spacing of 80 and 20 cm, respectively.

Table 1. Description of the 190 sweet stem sorghum varieties used in the study.

Name	Pedigree	Origin	Name	Pedigree	Origin	Name	Pedigree	Origin
AS1	KARI Mtama-1	Kenya	AS197	SA landrace LP 43	–	AS49	MN 1618 (tall selection)	–
AS10	MN 1812(Cream)	USA	AS198	SA landrace LP 44	–	AS5	Pirira	Malawi
AS100	Sefofo	–	AS199	SA landrace LP 45	–	AS50	MN 1705	–
AS101	ICSB 478	–	AS2	Thar	Kenya	AS51	MN 1812	–
AS102	RTX 436	–	AS200	SA landrace LP 46	–	AS52	MN 2365	–
AS103	MN 2332	–	AS202	SA landrace LP 48	–	AS53	MN 2500	–
AS105	FPR(168 × GS70)	–	AS203	SA landrace LP 49	–	AS54	MN 2622	–
AS106	KAT-369 × Makueni local	–	AS204	SA landrace LP 50	Polokwane	AS55	MN 4002 (short selection)	–
AS107	SDS 5232	–	AS205	SA landrace LP 51	–	AS56	MN 4002 (tall selection)	–
AS108	P9504B	–	AS219	Imfe Bulawayo	Zimbabwe	AS57	MN 4137	–
AS109	P9511B	–	AS240	AS19	Ukulinga	AS58	MN 4320 (short selection)	–
AS110	P9538B	–	AS241	AS79	Ukulinga	AS59	MN 4320 (tall selection)	–
AS111	P9539B	–	AS242	AS1	Ukulinga	AS6	Serena	Kenya
AS112	BKS24ms3/BON34	–	AS243	AS2	Ukulinga	AS62	MN 4519	–
AS113	TX2737/91BE7414	–	AS244	AS2	Ukulinga	AS63	Nus 34	–
AS114	BTx3197	–	AS245	AS77	Ukulinga	AS64	Nus 34 (2nd gen.)	–
AS115	BTx631	–	AS246	AS97	Ukulinga	AS65	P9528	–
AS116	01Aphid207	–	AS247	AS79	Ukulinga	AS66	BTX 378	–
AS117	01Aphid148	–	AS248	AS72	Ukulinga	AS67	SDL 89473	–
AS12	CR35.5 × IESV8820 × Serena	Kenya	AS249	AS72	Ukulinga	AS68	GV 3020	Zambia
AS121	Kat 369 × EX-1 Chira	–	AS250	AS97	Ukulinga	AS69	ICSR 91030	–
AS122	KSV 12	–	AS251	AS97	Ukulinga	AS7	Seredo	Kenya
AS124	02mn4034-(K70647-1-1/pl1	–	AS252	AS97	Ukulinga	AS70	SDS 3978	–
AS127	Tx2737	–	AS253	AS72	Ukulinga	AS71	Dwarf Wonder	–
AS128	Tx2883	–	AS254	AS72	Ukulinga	AS72	KAT-487	–
AS129	KARI Mtama 1 × ICS 3-1	–	AS255	AS72	Ukulinga	AS73	IRAT-204	–
AS13	Gadam El Hamam	Sudan	AS256	AS72	UKZN	AS74	ICSV 111	–
AS130	Gambella 1107	–	AS257	AS72	UKZN	AS75	SADC entry 35	–
AS131	WK#1025 Sudan	–	AS258	AS72	UKZN	AS76	CR35.5 × IS-882	–
AS132	Parc 1260793	–	AS259	AS72	UKZN	AS77	SDSH 90162	–
AS133	Marimanti Co 1110	–	AS260	AS72	UKZN	AS78	IS 8193 × AF 28	–
AS134	P6 NQ#23 Sudan	–	AS261	AS72	UKZN	AS79	P9513B	USA
AS135	Dinkmash	–	AS262	AS72	UKZN	AS80	P9521	–
AS136	FLO (107) × GS 3541	–	AS263	AS72	UKZN	AS81	P9526	–
AS137	IESV 92022 DL	–	AS264	AS13	Mtentu	AS82	ICSR 93034	India
AS138	Mugeta	–	AS265	AS79	Mtentu	AS83	ICSV 700	–
AS14	NUS 18 SA4455	South Africa	AS271	AS79 × SS27	Ukulinga	AS84	NTJ 2	–
AS140	Kaguru	–	AS28	NUS28 -SA4470	South Africa	AS85	Ent 64 DTN	–
AS141	Kiboko local	–	AS3	ZSV 3	Zambia	AS86	ICSV 574	–
AS143	Red Swazi	–	AS308	AS204 × AS138	Ukulinga	AS87	ICSB 323	–
AS145	AWN98	–	AS391	SS 27 C	Ukulinga	AS88	SDSL 89569	–
AS146	GV 3017	Zambia	AS393	AS138 × SS27	Ukulinga	AS89	Skalane	Zimbabwe
AS147	MRS94	–	AS395	AS103 × AS97	Ukulinga	AS9	Nus 35(2nd gen.)	–
AS148	SDS 3472	–	AS4	Macia	Mozambique	AS90	ICSB 724	–
AS15	Lanet	Kenya	AS41	MN 1408	–	AS91	ICSB 5	–
AS150	SDSL89572	–	AS42	MN 1435	–	AS92	SV 1	–
AS152	01MN1589	–	AS421	#5 235466	Ethiopia	AS93	SAR 29	India
AS153	Mul	–	AS43	MN 1439	–	AS94	ICSB731(ICSV1171BF)	–
AS154	SDSH 409	–	AS432	#14 235929	Ethiopia	AS95	SPV 1411	–
AS155	ICSV 91085 × IESV 9105 DL	–	AS434	#3 243684	Ethiopia	AS96	ICSB 4	–
AS158	964063 × Seredo	–	AS436	N-13strigaesistant	Ethiopia	AS97	E 36-1	Ethiopia
AS16	SDS 342	–	AS44	MN 1500	–	AS98	ICSVP 3046	–
AS161	KAT 369 × PP 290	–	AS440	White Degalet	Ethiopia	AS99	S 35	–
AS162	IESV 920220L	–	AS441	Red Degalet 1	Ethiopia	PEX40287	61(07S)-35	–
AS165	IS 155	–	AS442	Red Degalet 2	Ethiopia	SS120	Sugargraze	Australia
AS167	NUS 2 - SA4433	–	AS443	Red Degalet 3	Ethiopia	SS17	SS17	South Africa
AS17	ICSV 3	–	AS444	Yellow Degalet	Ethiopia	SS27	SS27	South Africa
AS173	Sinankhomo	Malawi	AS445	IBRO 1	Ethiopia	SS44	SS44	South Africa
AS18	Mexican R Line 5	Mexico	AS447	IS11167	Ethiopia	SS49	SS49	South Africa
AS19	Mexican R Line 15	Mexico	AS448	IS11758	Ethiopia	SS52	SS52	South Africa
AS192	Msinga imphe	–	AS45	MN 1557 (normal selection)	–	SS56	SS56	South Africa
AS194	Mtentu imphe	–	AS46	MN 1557 (late selection)	–	Urja	URJA	India
AS195	SA landrace LP 41	–	AS48	MN 1618 (short selection)	–	WD103	W Head 3	Netherlands
AS196	SA landrace LP 42	–

Note: – = Unknown origin.

Trial establishment and maintenance

Experimental genotypes were planted in seedling trays at the greenhouse facility of Controlled Environment Research Unit (CERU) at the University of KwaZulu-Natal. At four weeks after planting seedling plants were transplanted to Ukulinga Research Farm at the University of KwaZulu-Natal, in South Africa (29°37′S 30°22′E; 596 m above sea level). The trial was conducted during the 2015/2016 summer season (November to April). The trial site received 581 mm moisture through rainfall and supplemental irrigation. The average minimum temperature for the season was 10.2°C and the mean maximum temperature was 28.4°C. Experimental plots were fertilised at 375 kg ha⁻¹ with 2:3:2 (N:P:K) granular fertiliser. At the flowering stage plants were side dressed with 130 kg ha⁻¹ of Urea (46% N) granular fertiliser. After transplanting the trial was treated with 100 ml per ha of lamda cyhalothrin (Karate) to control a wide range of pests. When plants reached 60 cm height plots were treated with 120 ml per ha of lamda cyhalothrin to control pests.

Data collection

The following quantitative traits were measured during the study: days to flowering were counted as the date of seeding to the time that 50% of the plants started flowering (Vanderlip and Reeves 1972). Plant height was measured from the base of the plant to the tip of the panicle and expressed in cm at 50% flowering. Stem diameter (mm) was measured using a vernier calliper on the midsection of three plants. One row was harvested from each plot. Stalk biomass () was measured by removing leaves and heads, then cutting at ground level and weighing the stems at 50% maturity. Samples of chopped stalks were weighed green and re-weighed after oven drying at 60°C until constant mass was reached to gravitationally estimate dry mass (Hlophe 2014). Dry matter was calculated using the formula:(1) Fibre content was calculated using the formula:(2) Stalks were cut using a chaff cutter and a representative sample was analyzed for % Brix using cold digester by dividing the stem into three equal parts. Stem juiciness was scored stem brix was calculated as follows:(3) The final measurement was the mean of the three samples. Given the same brix reading, this calculation results in an upward adjustment of juicy cultivars over dry ones because at equal brix readings, juicier cultivars have more sugar compared to drier ones (Makanda 2009).

Ethanol productivity was calculated using the formulae below:(4) (5) (6)

Data analyses

General analyses of variance were performed for all quantitative data using the REML programme of GenStat 17th edition with number of plants as the covariate using a model by Cochran and Cox (1957). Multiple comparisons among variety means were conducted by least significant difference (LSD) test at 5% levels of significance. Cluster analysis was performed using Genstat 18th edition to establish genetic relationships among genotypes. Principal component analysis (PCA) based on the correlation matrix was performed using Genstat to identify influential traits for selection. A PCA bi-plot was plotted using GenStat to show the associations among genotypes based on observed traits. The Pearson's phenotypic correlation analysis was performed in GenStat 17th Edition (Payne et al. 2011) to describe the relationship among the morphological traits. Path coefficient analysis was used to calculate direct and indirect effects of traits on ethanol production using the PathSAS programme developed by Cramer and Wehner (2000). Variance components were partitioned using the restricted maximum likelihood (REML) approach in Genstat 17th. Heritability in the broad- sense was calculated using Hallauer and Miranda (1981) as follows:(7) where:  = broad-sense heritability;  = genotypic variance;  = phenotypic variance. The heritability % was categorised as low, moderate; and high in accordance with Robinson et al. (1949) as follows: 0–0.3, low; 0.3–0.6, moderate and >0.6, high. Genetic coefficient of variation (GCV) and phenotypic coefficient of variation (PCV) were computed according to Singh and Chaudhary (1979) and expressed as percentage as follows:(8) (9) where:  = genotypic variance;  = phenotypic variance;  = grand mean of trait . Predicted genetic gain (PG) was calculated by selecting 13% of the superior genotypes and calculated using the following formula (Singh and Chaudhary 1979):(10) where:  = selection differential (1.627) at 13% selection intensity;  = broad-sense heritability;  = phenotypic variance. Finally, predicted genetic gain % (PG %) was calculated as percent of the mean calculated according to Shukla et al. (2006):(11) where:  = grand mean of trait .

Results and discussion

Analysis of variance

Genotypes showed highly significant (p < 0.001) differences for all measured traits. The significant differences indicated the existence of high degree of variability among the genotypes (Tesfamichael et al. 2015) that could be exploited for sweet stem sorghum improvement.Mean response of test genotypes for eight bioethanol related traits.

Table 2 shows the mean response of test genotypes for eight bioethanol related traits. Days to flowering varied from 62 to 152 with a mean of 93 days. The genotypes with delayed maturity associated with increased biomass production were AS46, AS434, AS443, AS441 and AS205. Plant height varied from 90 to 420 cm with a mean of 236 cm. The tallest genotypes were AS442, AS443, AS447, AS448 and AS441. Stem diameter ranged from 7 to 31.33 mm with a mean of 16 mm. Genotypes with the thickest stalks included AS143, AS441, AS251, AS250 and AS442. Biomass yield varied from 6.668 to 111.2 t ha⁻¹ with a mean of 30 t ha⁻¹. Genotypes AS203, AS205, AS448, AS443, and AS251 had the highest biomass yield. Stalk dry matter content ranged from 17.2% to 44.2% with a mean of 29.8%, while fibre content varied from 8.92% to 34.8% with a mean of 17.2%. The stalk brix of genotypes varied from 3.3% to 18.9% with a mean of 12. %. Ethanol productivity ranged from 240.9 to 5500 l ha⁻¹ with a mean of 1886 l ha⁻¹.

Table 2. Means of eight bioethanol and related traits of 190 sweet stem sorghum genotypes.

Name	DTF	PH	SD	BM	DM	FC	SB	EP	Name	DTF	PH	SD	BM	DM	FC	SB	EP
AS203	106	345	22.8	83.8	27.5	13.5	13.5	5474	AS69	79.0	143	13.5	22.7	28.1	13.0	14.6	1639
AS391	116	305	13.7	62.8	28.1	13.1	14.5	4509	AS53	147.0	310	27.3	40.3	29.8	20.7	8.6	1621
AS205	148	350	23.5	70.5	26.2	18.6	14.1	4315	AS444	140.0	375	18.8	38.9	27.4	18.5	8.4	1618
AS251	136	385	29.2	64.2	31.1	17.5	13.1	4205	AS198	128.0	240	14.3	30.2	23.4	11.8	11.1	1615
AS448	132	405	27.5	66.7	31.8	20.0	11.3	3816	SS120	87.5	260	16.7	31.7	26.7	16.0	10.2	1598
AS44	130	300	19.2	56.1	31.2	17.0	13.8	3800	AS94	84.0	190	14.0	24.9	23.9	10.6	12.8	1571
AS56	133	280	17.3	51.1	30.0	14.6	14.9	3761	AS14	74.0	190	13.5	29.1	22.7	11.4	10.8	1554
AS246	87	305	17.2	48.7	33.0	17.2	15.3	3743	AS16	77.0	285	13.3	28.3	30.9	19.4	11.0	1542
AS105	89	235	15.8	47.5	29.3	13.4	15.4	3619	AS162	77.0	160	17.8	24.6	29.4	16.3	12.6	1532
AS59	106	285	20.8	54.7	32.3	18.3	13.5	3574	AS442	136.5	410	27.8	53.6	29.5	23.3	5.7	1524
AS111	101	240	13.8	47.8	29.8	14.2	15.1	3570	AS202	89.5	240	17.2	31.4	22.2	12.1	9.6	1519
AS113	85	210	18.5	43.4	30.6	13.7	16.4	3548	AS252	114.0	345	24.8	37.3	33.0	24.2	8.3	1517
AS253	107	315	22.8	53.3	25.8	12.2	13.2	3476	AS85	82.5	180	15.0	19.9	29.4	13.6	15.3	1494
AS254	99	330	19.8	57.7	27.6	15.2	12.0	3418	AS440	147.0	365	22.3	41.0	30.5	22.6	7.4	1481
AS79	113	210	21.2	43.0	30.2	14.2	15.6	3313	AS248	86.0	310	21.5	30.9	25.0	15.0	9.6	1462
AS195	107	270	18.3	51.3	25.4	11.9	13.0	3310	AS265	107.5	330	15.3	32.6	35.0	25.8	8.7	1451
AS58	105	290	19.8	47.2	30.5	16.0	14.1	3286	AS3	76.5	220	14.7	25.6	32.3	20.4	11.4	1449
AS255	127	360	20.0	48.5	28.7	14.5	13.7	3278	AS247	90.0	320	22.5	28.2	41.6	30.7	10.4	1446
AS82	101	230	10.3	39.7	33.0	15.8	16.7	3260	AS436	81.0	210	11.8	23.3	27.7	14.9	12.3	1410
AS127	82	245	9.0	42.0	29.1	13.2	15.5	3181	AS146	91.5	190	18.2	23.3	31.5	19.1	11.9	1395
AS245	131	305	24.2	48.7	25.5	12.2	12.8	3152	AS88	85.0	150	12.8	18.7	29.5	14.1	14.9	1363
AS74	75	220	14.8	36.9	33.0	15.5	17.1	3117	AS421	121.5	295	20.0	31.3	35.8	26.7	8.7	1341
AS98	127	250	19.0	44.5	29.1	14.5	14.1	3105	AS441	148.0	400	30.5	46.1	25.9	19.6	5.8	1326
AS443	149	405	23.8	65.0	30.7	21.0	9.2	3021	AS77	73.0	185	11.3	20.7	28.4	14.7	13.2	1324
AS78	128	275	19.7	42.7	41.8	27.1	14.2	3021	AS52	106.0	370	20.7	36.8	33.3	25.5	7.3	1297
SS27	97	275	13.3	42.0	30.8	15.8	14.6	3008	AS18	86.0	170	14.3	19.0	27.6	13.6	13.5	1272
AS83	95	245	13.5	40.3	29.2	14.3	14.4	2869	AS51	86.5	305	20.7	27.2	34.0	24.3	9.2	1264
AS45	119	345	17.8	47.1	24.3	11.5	12.3	2864	AS19	81.5	193	12.7	20.0	27.6	14.5	12.6	1242
AS447	141	405	23.3	51.3	30.2	18.5	11.2	2813	AS1	83.5	170	13.3	19.2	28.3	14.8	13.0	1236
AS86	107	230	20.7	37.0	32.9	17.3	15.1	2770	AS271	104.0	280	17.8	30.8	20.1	11.5	8.1	1230
AS10	92	310	20.0	49.6	38.5	26.5	11.5	2723	AS243	120.0	330	20.7	36.3	35.3	28.0	6.8	1227
AS122	86	200	13.8	35.6	30.9	15.1	15.3	2674	AS55	88.0	170	12.3	20.5	29.9	17.4	12.0	1214
AS103	88	250	13.0	43.2	27.3	14.4	12.4	2667	SS44	78.0	190	13.3	17.8	29.5	15.1	13.9	1203
AS158	100	185	12.0	36.3	31.2	15.7	15.0	2655	AS89	85.0	200	8.8	27.8	20.0	10.6	8.9	1201
AS244	87	290	19.5	39.6	27.5	14.0	13.0	2548	AS15	125.5	275	22.2	29.9	34.1	25.7	7.9	1195
AS95	106	255	16.2	35.7	30.1	15.1	14.5	2547	AS197	112.5	230	13.5	23.2	23.9	13.6	9.8	1173
AS204	87	255	12.2	37.3	26.4	12.4	13.5	2517	AS242	82.0	265	26.0	23.1	25.2	14.5	10.2	1169
AS97	86	175	15.0	33.7	28.7	13.4	14.8	2471	AS109	85.0	140	12.8	18.2	29.1	15.7	12.9	1159
AS2	80	185	11.3	41.7	22.6	10.3	11.9	2448	AS100	86.0	255	17.5	25.9	35.4	26.0	8.9	1134
AS72	87	185	14.3	27.0	33.3	15.0	17.8	2391	PEX40287	76.0	165	18.0	18.8	23.0	12.0	10.5	1131
AS445	134	390	21.7	46.4	34.4	23.5	10.4	2387	AS432	114.5	295	18.8	26.7	34.3	25.2	8.6	1127
AS108	84	245	20.0	31.5	34.6	18.8	15.3	2386	AS80	80.0	140	8.8	17.3	26.6	13.3	12.8	1096
AS70	78	225	15.2	33.9	34.6	20.2	13.9	2348	AS154	83.5	165	15.3	19.4	26.6	14.8	11.3	1085
AS106	87	300	23.3	39.2	30.2	17.7	12.0	2326	AS150	78.5	180	8.8	24.2	23.9	14.5	9.0	1084
AS395	114	305	18.3	45.3	25.2	14.8	9.9	2321	AS65	79.0	135	10.2	16.2	27.5	13.6	13.5	1071
AS199	84	220	16.0	33.3	29.1	14.6	14.0	2301	AS117	84.5	140	12.5	18.4	26.0	13.8	11.8	1065
AS200	80	235	10.8	34.9	27.6	13.8	13.4	2285	AS165	74.0	160	12.8	18.9	32.3	20.6	11.2	1046
AS240	106	280	15.8	32.0	31.4	16.4	14.5	2285	AS262	83.0	190	14.2	20.0	27.1	16.1	10.5	1038
AS101	89	200	13.8	32.3	29.3	14.7	14.1	2256	AS140	73.0	205	10.0	16.4	39.5	26.3	12.7	1014
AS128	77	180	18.3	28.4	33.2	16.7	16.0	2236	AS145	103.0	290	13.8	30.0	36.0	29.1	6.4	1014
Urja	97	295	15.3	27.4	31.5	14.8	16.2	2193	AS28	62.0	200	11.0	15.9	32.9	19.7	12.7	995
AS259	87	290	18.8	35.9	28.2	15.5	12.3	2185	AS167	85.0	285	13.7	29.3	34.8	27.9	6.4	957
AS147	86	190	19.3	26.3	30.6	13.4	16.7	2178	AS99	75.0	170	9.8	14.2	29.3	16.3	12.6	919
AS258	75	170	11.3	32.6	28.6	14.6	13.5	2176	AS5	81.0	135	11.3	16.6	23.8	12.1	11.2	916
AS84	85	205	8.7	28.8	30.8	15.0	15.3	2176	AS173	85.5	315	15.2	23.8	40.2	32.2	7.5	909
AS41	129	340	16.2	38.6	29.7	17.7	11.6	2154	AS68	79.0	140	9.0	12.6	27.5	12.4	14.6	906
AS263	92	290	13.7	34.0	28.6	16.3	11.9	2134	AS393	106.0	280	16.0	24.7	23.2	15.7	7.0	887
AS148	79	185	17.0	27.8	29.6	13.6	15.5	2129	AS73	82.5	123	7.3	12.1	25.8	11.0	14.3	851
AS87	88	160	12.5	27.6	30.2	14.2	15.5	2121	AS194	74.0	160	11.0	13.7	38.1	25.1	12.5	849
AS7	84	180	18.3	27.0	34.1	17.8	15.8	2113	AS64	74.5	185	12.3	18.5	31.4	21.6	9.3	848
SS17	74	275	14.0	28.2	30.8	15.0	15.3	2098	AS4	80.0	125	13.5	19.2	20.4	10.6	9.3	842
AS196	100	270	13.7	28.0	30.2	14.4	15.3	2096	AS71	73.5	120	8.2	18.7	27.2	17.6	9.2	837
AS6	93	195	21.0	28.8	33.4	18.0	14.9	2083	AS81	90.5	120	15.2	18.4	22.2	12.9	8.8	803
AS107	81	215	12.5	28.7	32.3	17.4	14.4	2045	SS56	68.0	170	9.2	15.4	28.7	18.1	10.1	770
AS260	90	295	22.2	36.2	33.8	21.9	11.4	2037	AS116	85.5	110	11.0	19.7	22.2	13.8	7.9	769
AS112	88	160	17.3	24.6	35.5	18.5	16.5	2011	AS91	81.0	130	10.0	15.9	25.7	16.1	9.2	736
AS155	83	200	12.2	23.8	37.2	19.9	16.9	1993	SS49	76.5	170	13.5	8.9	33.0	24.4	8.1	716
AS135	82	190	13.3	24.6	29.9	13.1	16.3	1987	AS62	83.5	225	12.5	29.5	18.7	13.4	4.9	707
AS131	79	205	13.0	25.2	31.0	14.7	15.8	1942	AS42	120.0	270	18.3	20.5	39.9	32.2	7.2	701
AS249	88	295	25.0	36.1	23.3	12.3	10.5	1925	AS43	119.0	275	17.0	19.7	34.1	26.5	7.2	672
AS136	82	190	18.2	25.7	31.7	16.1	15.1	1924	AS13	76.5	125	9.2	11.0	24.8	12.1	12.2	663
AS121	88	215	11.3	28.4	32.5	18.5	13.5	1922	AS49	77.5	190	8.7	12.9	32.8	22.7	9.6	615
AS48	131	295	18.5	34.0	35.4	23.5	11.4	1919	AS114	78.0	120	8.3	14.5	22.7	14.2	8.0	605
AS129	79	190	13.8	24.1	31.7	15.3	15.9	1899	AS9	84.0	160	9.3	17.8	23.8	16.5	6.8	599
AS110	87	150	8.3	36.4	25.7	14.7	10.5	1889	AS57	86.5	200	14.0	12.7	27.5	18.0	9.0	567
AS241	122	300	21.7	33.5	33.4	21.5	11.4	1887	AS264	83.5	335	12.8	18.1	30.5	23.7	6.3	556
AS75	74	215	9.8	27.7	33.3	18.9	13.9	1883	AS115	92.5	110	8.7	17.1	26.0	19.8	5.7	482
AS141	70	175	14.0	25.3	31.2	15.6	15.2	1862	AS96	86.0	125	8.5	11.7	21.0	12.3	8.3	481
AS17	75	190	14.3	29.8	25.8	12.9	12.4	1853	AS124	84.0	95	12.2	10.6	18.7	12.5	5.7	363
AS143	84	195	31.0	27.9	26.1	12.4	13.3	1829	AS54	64.5	195	11.0	10.1	30.5	25.0	5.0	309
AS250	88	305	29.0	35.3	25.8	14.8	10.6	1816	AS12	74.5	165	12.0	8.4	38.5	31.9	6.1	241
AS137	83	190	13.5	24.1	30.8	15.1	15.2	1815	AS102	83.5	*	12.2	*	*	*	*	*
AS92	81	155	14.2	25.0	29.2	14.1	14.7	1792	AS152	78.5	90	10.8	7.8	*	*	*	*
AS134	86	210	18.3	28.3	31.2	18.3	12.4	1781	AS161	83.0	*	17.7	*	*	*	*	*
AS50	95	335	21.3	39.8	31.7	22.2	9.1	1777	AS192	79.0	200	10.5	15.5	*	*	*	*
AS434	150	360	18.0	42.0	26.8	17.7	8.6	1773	AS219	96.5	290	17.5	*	*	*	*	*
AS67	78	160	15.8	22.6	33.4	16.8	16.1	1769	AS256	72.5	230	9.7	8.4	*	*	*	*
AS63	78	220	15.3	23.8	31.4	16.0	14.9	1758	AS257	73.0	200	9.5	6.7	*	*	*	*
AS90	81	155	14.3	23.0	29.0	13.3	15.2	1744	AS261	68.5	180	14.3	16.6	*	*	*	*
AS76	72	205	24.3	23.5	36.5	21.0	15.0	1741	AS93	103.0	*	12.0	*	*	*	*	*
AS66	77	172	13.7	23.4	29.2	13.6	15.1	1734	SS52	73.5	130	9.7	8.8	*	*	*	*
AS308	94	295	15.3	31.5	26.4	14.8	11.1	1733	WD103	62.5	220	13.7	7.9	*	*	*	*
AS130	82	225	14.3	24.4	30.9	16.5	13.9	1721	Mean	92.9	236	15.8	30.1	29.8	17.2	12.1	1886
AS153	81	210	11.2	25.8	30.8	16.9	13.5	1720	Std D	20.9	4	5.0	14.4	4.8	5.2	3.2	1006
AS46	151	310	20.0	37.0	35.3	25.4	9.5	1717	Min	62.0	90	7.0	6.7	17.2	8.9	3.3	240.9
AS133	82	205	17.7	24.9	28.6	14.2	13.9	1693	Max	152.0	420	31.3	111.2	44.2	34.8	18.9	5474
AS138	73	190	12.8	22.6	31.1	15.2	15.5	1688	% CV	7.5	980	4.3	23.2	10.3	17.2	13.8	28.7
AS132	79	190	19.0	20.9	31.0	14.4	16.1	1653	LSD	19.7	64	1.9	19.5	8.6	8.3	4.7

Note: DTF = days to flowering, PH = plant height, SD = stem diameter, BM = biomass, DM = dry matter, FC = fibre content, SB = stalk brix, EP = ethanol, Std D = standard deviation, Min = minimum, Max = maximum, CV = coefficient of variation, LSD = least significant difference.

Bold text denote high ethanol producing genotypes selected at 13% selection intensity, *signifies missing results.

The best ethanol yielding genotypes were AS203, AS391, AS205, AS251 and AS448 providing mean yields of 5474, 4509, 4315, 4205 and 3816 l ha⁻¹, in that order. Genotype AS203 produced over at least 20% ethanol greater than all other genotypes. This can be attributed to the fact AS203, being a South African landrace is well adapted to the environment. The top ethanol producing genotype AS 203 also had the highest biomass yield of 84 t ha⁻¹. However, this genotype was slightly shorter, thinner and had lower stalk brix than genotype AS205. A similar trend was observed for the bottom yielding genotype. Genotype AS12 was the lowest yielding only in terms of ethanol production. But genotype AS 12 was thicker, taller and late maturing than genotypes AS71 and AS194. The trend in ethanol production was not affected by other quantitative traits.

Ethanol productivity of some of the tested genotypes were similar to the yields reported by Regassa and Wortmann (2014). The authors reported the highest ethanol productivity that can be anticipated from sweet stem sorghum as 6000 l ha⁻¹. However, stalk brix, plant height and stem diameter of the experimental genotypes did not reach the maximum of 24%, 480 cm and 45 mm respectively reported by the same authors. Wide variability in plant height (90–270 cm) and days to flowering (50–80 days) were reported by Tesfamichael et al. 2015. Elangovan et al. (2014) reported large variation among genotypes in days to flowering (68–100 days), plant height (232–497 cm) and stalk brix (4%–22%). Lekgari and Dweikat (2014) also reported a wide range in days to anthesis of 70–147 days, and plant height of 76–423.8 cm.

Ample genetic variability was detected among the tested sweet stem sorghum genotypes allowing for selection for all the measured traits. Most traits had a low standard deviation (SD) from the mean, with plant height having an SD of 4 cm from a mean of 240 cm and stem diameter having an SD of 4.9 cm from a mean of 15.8 cm. On the other hand, biomass yield had a relatively high SD of 15 t ha⁻¹ from a mean of 30 t ha⁻¹. A relatively low coefficient of variation (<17%) was detected for all traits except for biomass and ethanol production. The high coefficient of variation for both biomass and ethanol production were higher denoting susceptibility to environmental factors influencing their expression to some degree (Sinha and Kumaravadivel 2016). The selected top 13% of genotypes produced ethanol yields ranging from 3000 to 5500 l ha⁻¹ (Table 2). On the other hand, the bottom 13% produced only 240–880 l ha⁻¹.

Clustering of sweet stem genotypes based on eight bioethanol related traits

Table 3 presents clustering of test genotypes. Most high ethanol producing genotypes were allocated in Cluster III. Sub-clusters a and b in Cluster III contained genotypes that did not produce much ethanol. All Sub-cluster III-b genotypes were among the top five high ethanol producing genotypes. All the sub-clusters had at least 1 high ethanol producing genotype (in the top 13%) except Sub-clusters I-a, I-b and II-a. This is an indication of a high level of morphological diversity among the tested population. Sub-cluster II-a contained three of the five late maturing genotypes (AS434, AS441, AS443) and four of the five tallest genotypes (AS441, AS442 AS 443, AS 447).

Table 3. Clusters and sub-clusters of 190 sweet stem genotypes based on eight bioethanol related traits.

Cluster	Sub-cluster	Name of genotype
I	a	AS1, AS109, AS110, AS114, AS115, AS116, AS117, AS124, AS13, AS14, AS146, AS147, AS148, AS150 AS152 AS154, AS17, AS18, AS19, AS2, AS262, AS4, AS436, AS5, AS55, AS57, AS62, AS65, AS68, AS73AS77, AS80, AS81, AS89, AS9, AS91,AS94, AS96, AS99, PEX40287, SS44, SS52, SS56, AS71
	b	AS12, AS140, AS165, AS192, AS194, AS256, AS257
	c	AS101, AS102, AS103, AS105, AS107, AS108, AS111, AS112, AS113, AS121, AS122, AS127, AS128, AS129, AS130, AS131, AS132, AS133, AS134, AS135, AS136, AS137, AS138, AS141, AS153, AS155, AS158, AS16, AS196, AS199, AS200, AS204, AS240, AS258, AS3, AS6, AS63, AS66, AS67, AS69, AS7, AS70, AS74, AS75, AS76, AS82, AS83, AS84, AS85, AS87, AS88, AS90, AS92, AS93, AS95, AS97, AS162, AS161, AS72, SS17, SS27, Urja
	d	AS106, AS197, AS198, AS202, AS219, AS242, AS244, AS248, AS249, AS250, AS259, AS263, AS271, AS308, AS393, SS120
II	a	AS41, AS434, AS440, AS441, AS442, AS443, AS444, AS445, AS447, AS53
	b	AS78
	c	AS10, AS100, AS145, AS15, AS167, AS254, AS260, AS264, AS265, AS42, AS173, AS241, AS243, AS247, AS252, AS421, AS43, AS432, AS46, AS48, AS50, AS51, AS52
III	a	AS246, AS391, AS58, AS59, AS79, AS86
	b	AS203, AS205, AS251, AS448
	c	AS195, AS245, AS253, AS255, AS45, AS56, AS98, AS395, AS44
IV	a	AS143

Phenotypic correlation among bioethanol related traits

The correlation coefficients of the eight quantitative traits assessed are presented in Table 4. Correlations among quantitative traits were analyzed in terms of significance, strength and nature of correlation. Several traits showed highly significant (p ≤ 0.001) correlations such as days to flowering with all traits; biomass with ethanol yield and plant height; ethanol with all traits except dry matter; plant height with all traits. Kumar et al. (2012) reported that plant height exhibited significant positive associations with days to flowering, and brix. A strong correlation was recorded between biomass with ethanol production (r = 0.83). Biomass was strongly correlated to days to flowering, ethanol production, plant height and stem diameter. These results were in agreement with Ganesh et al. (1995) and Prasad et al. (2013). Their studies indicated that high biomass with total sugar content is a pre-requisite for high ethanol production. Hence, these traits could be utilised in the sweet sorghum breeding programmes for ethanol production. Selection for these traits would help in achieving highest ethanol productivity. Other strong correlations were detected between days to flowering with plant height and stem diameter, plant height with stem diameter, fibre content with dry matter. From these results, it is evident that these traits are associated with ethanol production and are inter-correlated among them. Thus, the selection in any one of these ethanol yield attributing traits will lead to increase in the other traits, thereby enhancing ethanol production. Stalk brix had a relatively weak positive association with biomass, agreeing with the reports of Bangarwa et al. (1989) and Kumar et al. (2012).

Table 4. Pearson's correlation coefficients, among quantitative traits of sweet stem sorghum genotypes (n = 190).

	DTF	PH	SD	BM	DM	FC	SB	EP
DTF
PH	0.7285**
SD	0.5948**	0.6877**
BM	0.6653**	0.7331**	0.6014**
DM	0.0765	0.2381**	0.3726*	0.041
FC	0.2386**	0.1714**	0.2198**	−0.0281	0.7811**
SB	−0.2339**	−0.2272**	−0.0824*	0.1411	0.1886**	−0.4564**
EP	0.3727**	0.4427**	0.3777**	0.8335**	0.109	−0.2403**	0.5701**	–

Note: DTF = days to flowering, PH = plant height, SD = stem diameter, BM = biomass, DM = dry matter, FC = fibre content, SB = stalk brix, EP = ethanol.

*Significant at 5%, **significant at 1%.

Path coefficient analysis

Table 5 is a presentation of the direct and indirect effects of the component traits on ethanol productivity. A few traits exhibited significant (p ≤ 0.001) direct effects on ethanol production including plant height, stem diameter and biomass. Biomass had the greatest direct effect on ethanol production. Hence, it may be concluded that these traits could enhance ethanol production more than other traits. Days to flowering, plant height, stalk brix and stem diameter showed the greatest indirect effects on ethanol production through biomass. Similar results have been reported by Kumar et al. (2012). Since biomass had the highest direct effect on ethanol production, these traits should be considered during future selection programmes. This is in agreement with Naphade (1972), Potdukhe et al. (1994) and Kumar et al. (2012) who reported that days to flowering had an indirect positive association with biomass through plant height, while plant height had indirect positive effect on biomass through days to flowering, which indicated that there is a real association between days to flowering and plant height. Hence, these traits should be considered during progeny selection programme. The strongest indirect effect on ethanol production via biomass was exerted by plant height. The strongest indirect effects on ethanol production via dry matter was fibre content and vice versa.

Table 5. Path coefficients showing direct (bold) and indirect effects of quantitative traits on ethanol production in 190 sweet stem sorghum genotypes.

Traits	DTF	PH	SD	BM	DM	FC	SB
DTF	−0 . 052	0.005	−0.041	0.559	−0.005	0.020	−0.114
PH	−0.038	0 . 007*	−0.047	0.616	−0.016	0.031	−0.110
SD	−0.031	0.005	−0 . 068 *	0.506	−0.012	0.018	−0.040
BM	−0.035	0.005	−0.041	0 . 841**	−0.003	−0.002	0.069
DM	−0.004	0.002	−0.012	0.034	−0 . 068	0.065	0.092
FC	−0.012	0.003	−0.015	−0.024	−0.053	0 . 083	−0.222
SB	0.012	−0.002	0.006	0.119	−0.013	−0.038	0 . 486

Note: DTF = days to flowering, PH = plant height, SD = stem diameter, BM = biomass, DM = dry matter, FC = fibre content, SB = stalk brix.

*Significant at 5%, **significant at 1%.

Principal component analysis (PCA)

Table 6 is a presentation of the rotated component matrix that displays the allocation of the entire variance explained by various principal components and their associations with quantitative traits. Principal component analysis suggested that only 3 first components (eigen value > 1) are important, and account for 88% of the total variation. This result differed from the studies of Mujaju and Chakauya (2008), Ali, Jabran et al. (2011), and Tesfamichael et al. (2015) who reported on different agro-morphological traits in sweet stem sorghum. Ali, Jabran et al. (2011) reported that out of a total of 21, seven principal components were extracted having Eigen value > 1 and these seven PCs contributed 77.653% of the total variability amongst the sorghum genotypes assessed for various morpho-physiological traits. Tesfamichael et al. (2015) reported that out of the seven PCs, the first 4 explained the majority of the total variation. These four PCs with Eigen value > 1 contributed 74.6% of the total variability amongst the sorghum genotypes assessed for various morpho-physiological traits. In the present study principal component-1 (PC-1) and PC-2 were highly significant, contributing 71% to the total variation. The sign of the loading signifies whether the relationship is positive or negative. The first principal component, which accounted for about 45% of the variation, was strongly associated with plant height, stem diameter, biomass and days to flowering. Therefore selection of tall, thick, high biomass and late maturing genotypes is essential for the breeding programme. Fibre content had a high positive loading into the second principal component while ethanol yield and stalk brix had negative loading into the second principal component. Traits with a high correlation to the third principal component in a negative direction were dry matter and stalk brix. Principal component-3 explained only 18% of the total variation.

Table 6. Rotated component matrix of 8 phenotypic traits of 190 sweet stem sorghum genotypes.

Trait	PC-1	PC-2	PC-3
Days to flowering	0.43148	0.08430	0.25804
Plant height	0.47908	0.13052	0.11948
Stem diameter	0.41913	0.05609	0.09837
Biomass	0.47148	−0.24558	0.05053
Dry matter	0.16168	0.32890	−0.69421
Fibre content	0.16176	0.60491	−0.27921
Stalk Brix	−0.00608	−0.49564	−0.54753
Ethanol	0.36606	−0.43893	−0.21733
Explained variance (eigenvalue)	3.548	2.121	1.397
Proportion of total variance (%)	44.35	26.51	17.47
Cumulative variance (%)	44.35	70.86	88.33

Note: PC = Principal component.

Figure 1 is a presentation of the principal component bi-plot analysis of eight phenotypic traits of 190 sweet stem sorghum genotypes. The bi-plot is illustrative of the associations of the phenotypic traits and the genotypes, with the principal components. According to Mwadzingeni et al. (2016) a high correlation of traits in discriminating genotypes can be observed from traits that have small angles between dimension vectors in the same direction. In the current study, such relationships can be observed among stem diameter, plant height and days to flowering, between ethanol, biomass and stalk brix and between fibre content and dry matter. Genotypes excelling in a particular trait were plotted closer to the vector line and further in the direction of that particular vector, often on the vertices of the convex hull (Mwadzingeni et al. 2016). According to the bi-plot the following genotypes: AS391, AS203 and AS205, were the best performers for ethanol yield and biomass because they were closest to these traits and located on the convex hull. Most of the genotypes were scattered in the negative sides of PC-1 and PC-2.

Figure 1. Bi-plot analysis of eight phenotypic traits of 190 sweet stem sorghum genotypes evaluated.

Genetic parameters

In this study all the traits had high heritability (>0.5) (Table 7). According to Insan et al. (2016), the characters that have broad genetic variability and high heritability estimates could be influenced by additive gene action. High heritability may not result on high levels of genetic gain (Johnson et al. 1955). Therefore it is necessary to oversee heritability estimates alongside genetic gain. In this case, high heritability estimates did not guarantee high predicted gain as the low predicted gain of <30% for most traits did not match the high heritability of >0.8 for most traits. Similar results of low predicted gain were observed by Insan et al. (2016) and such results may be attributed to the non-additive gene effects. On the other hand, high heritability coupled with high levels of genetic gain for traits such as ethanol productivity signified the existence of additive gene action, hence strong response to selection for ethanol productivity (Panse and Sukhatme 1964, 1989). Medium heritability and low genetic gain indicate the presence of epistatic gene action (Elangovan et al. 2014).

Table 7. Predicted genetic gain for eight bioethanol related traits of sweet stem sorghum genotypes selected at 13% selection intensity.

Traits		h^2	GCV	PCV	Predicted gain	Predicted gain %
Days to flowering	277.23	0.91	17.93	18.76	25.89	27.87
Plant height	0.25691	0.91	21.51	22.57	0.79	33.37
Stem diameter	20.9478	0.99	29.00	29.19	7.40	46.89
Biomass	128.26	0.82	37.69	41.53	16.72	55.64
Dry matter	0.001415	0.79	12.62	14.24	0.05	18.20
Fibre content	0.001594	0.80	23.21	25.95	0.06	33.77
Stalk Brix	6.522	0.85	21.09	22.85	3.84	31.67
Ethanol	663,924	0.80	43.20	48.21	1188.08	62.99

Note: , h² = heritability, GCV = Genetic coefficient of variation, PCV = Phenotypic coefficient of variation.

Burton (1952) advocated for the use of genotypic coefficient of variation (GCV) in addition to the heritability estimate in advancing the efficacy of the selection. The phenotypic coefficient of variation (PCV) was higher than the GCV for all traits, indicating the role of environmental factors influencing the expression of the traits to some extent (Godbharle et al. 2010; Elangovan et al. 2014). Differences in coefficients of variations (both genotypic and phenotypic) were exhibited with the GCV ranging from 12% to 43.2% and PCV varying from 14% to 48.2%. Considering ethanol productivity, the high GCV and PCV values suggested a strong opportunity to select superior genotypes for ethanol productivity. The lower or equal GCV and PCV observed for dry matter, plant height and days to flowering signified that enhancement of these particular traits would be limited (Elangovan et al. 2014). Similar results of equal GCV and PCV for plant height in sweet stem sorghum genotypes have also been reported by other researchers (Reddy et al. 2009).

The present study selected the best ethanol yielding genotypes including AS203, AS391, AS205, AS251 and AS448 that provided mean yields of 5474, 4509, 4315, 4205 and 3816 l ha⁻¹ in that order. Days to flowering, plant height, stalk brix and stem diameter had the strongest indirect effect on ethanol production through biomass. Since biomass had the strongest direct effect on ethanol production, these traits should be considered during a selection programme.

Acknowledgements

We want to thank to thank Ian Doidge and staff of the African Center of Crop Improvement, University of KwaZulu Natal for help with field work..

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes on contributors

Precious Mangena is a Ph.D. candidate at the University of KwaZulu-Natal. Presently, she is involved on developing superior sweet stem sorghum genotypes for bio-ethanol production.

Hussein Shimelis is a Professor of Plant Breeding at the University of KwaZulu-Natal in South Africa. He is a co-author for the current study.

Mark Laing is a Professor of Plant Pathology at the University of KwaZulu-Natal in South Africa. He is a co-author for the current study.

Additional information

Funding

We appreciate the financial support received from the Technology Innovation Agency (TIA) for the study.