Agro-morphological performance, nutritive value and dry matter yield stability of mission grass (Pennisetum polystachion (L.) Schult) genotypes in multi-environment trials in Western Oromia, Ethiopia

Abstract

Mission grass (Pennisetum polystachion (L.) Schult) is a promising forage crop due to its adaptability and productivity. Feed scarcity, both in quantity and quality, is a major constraint to animal production, limiting productivity and sustainability in livestock systems. Addressing this challenge requires the development and adoption of improved forage technologies to bridge feed gaps. This study was carried out to evaluate and identify high herbage dry matter yield, stable and better nutritional quality of Mission grass under varying environmental conditions in Western Oromia.Four Mission grass genotypes were evaluated against the standard check (Nechsare) across three locations (Haro Sabu, Kombo and Ayira) during the 2021–2022 main cropping seasons. The experiment was laid out in a randomized complete block design (RCBD) with three replications. Data such as plant height, number of tiller and leaves, leaf to stem ratio and herbage dry matter yield were collected, the nutritional quality was analyzed based on standard procedures and the collected data were subjected to analysis of variance (ANOVA). The Additive Main Effects and Multiplicative Interaction (AMMI), AMMI stability value, Genotype selection index and genotype and genotype by environment interaction (GGE) models were used to assess genotype-by-environment (G×E) interactions and stability. The measured and collected data of the genotypes were analyzed using SAS and the mean separation of least significance difference (LSD). Combined analysis of variance revealed highly significant differences (p < 0.01) among genotypes, environments, and their interactions for agronomic traits and herbage dry matter yield. In the AMMI analysis, the environmental factors contributed the largest proportion (58.20%) of the total variation, followed by genotype (26.82%) and G×E interaction (14.98%). Among the tested genotypes, NG-0121 produced the highest mean herbage dry matter yield (8.79 tha^− 1), followed by NG-0057 (7.35 tha^− 1). Stability analysis of AVS and GSI confirmed that NG-0121 and NG-0057 were high-yielding and relatively stable, outperforming the standard check by 43.86% and 20.29%, respectively. Additionally, these genotypes exhibited favorable crude protein content and digestibility. The study identified NG-0121 and NG-0057 as superior Mission grass genotypes with high yield potential, relatively stable, and desirable nutritional attributes. Given their high yield advantage over the standard check (Nechsare) and adaptability across different environments, NG-0121 and NG-0057 genotypes are recommended for variety verification and potential release in Western Oromia and similar agro-ecologies to enhance forage availability and livestock productivity.

Introduction

Mission grass (Pennisetum polystachion (L.) Schult) is a fast-growing perennial grass, reaching up to 2 m in height, with a fibrous root system well suited to infertile sandy soils and tropical grasslands [1]. It thrives in temperatures between 15 and 35 °C and requires over 500 mm of annual rainfall. As a promising forage crop, It is used for animal feed, producing 7–12 tha^{− 1} of dry matter (DM) and 500–1000 kgha^{− 1} of seed yield [2].

Animal production is the main component of agriculture that can support the livelihoods and security of large numbers of people in a developing country [3]. However, insufficient animal production is mainly due to less nutritional and management practices, disease and parasitic occurrence, low productivity and genetic potential, lack of extension services [4]. Among these constraints, inadequate quantity and quality feedstuff was identified as a major limiting factor to the development of livestock production and productivity [5], and the most bottleneck of livestock farming in Ethiopia is the shortage of livestock feeds in terms of amount and quality, especially during the dry season [6].

Ethiopian livestock primary rely on low-quality natural pastures and crop residues [7], leading to reduced animal productivity due to poor nutrition and low feed intake. Particularly, in study area (Western Oromia), imbalanced and inadequate feed supply for the existing livestock was available due to many challenges that limit the demand for the livestock feeds. Among the challenges is lack of improved, high yielding and broadly adaptable forage genotypes. This forces the farmers rearing their livestock on natural pasture/ grazing and crop residues (poor quality feed resources). Therefore, this highlights the need for targeted efforts to develop improved, high yielding forages varieties with better nutritional quality and adaptability to biotic and abiotic stresses. Furthermore, evaluating the performance and stability of Mission grass genotypes across multiple environments in the study area may fill the improved forage gaps.

Plant breeders prioritize developing widely adaptable crop varieties [8]. Their selection process emphasizes adaptability and consistent (stability) yield across diverse environments rather than maximizing yield in specific locations [9]. Multi-environment yield trials should focus on genotype by environment interaction (G×E), and analyzed using various statistical methods and software [10, 11], provide crucial insights into these traits. Breeders employ diverse analytical approaches to understand genotype-by-environment (G×E) interaction.

Commonly used statistical models, such as the additive main effects and multiplicative interaction (AMMI) model and the genotype and genotype by environment interaction (GGE) model, help assess genotype adaptability and stability across different environments. These models are often visualized using biplots [12], which aid in identifying genotype response patterns [13]. However, research on G×E interaction in Mission grass remains limited. This study aimed to identify high herbage yielding, stable and better quality genotypes which are suitable for release in Western Oromia and similar agro-ecologies in Ethiopia.

Materials and methods

Description of the study areas

The field experiments were conducted at Haro Sabu, Kombo, and Ayira of West and Kellam Wallaga zones during the 2021–2022 cropping seasons under rainfed conditions (Table 1).

Table 1.

Geographical location of the experimental sites

Experimental sites	Geographical locations			Rain fall (mm)	Soil types
	Altitude (m a.s.l)	Latitude	Longitude
Haro Sabu	1516	80 53’ N	35013’ E	1200–2350	Black and red soil
Kombo	1509	8051’ N	350 06’ E	1200–1800	Red soil
Ayira	1696	9003’ N	350 23’ E	800–1600	Red soil

Experimental materials and design

The experimental materials used for this study were sourced from the locally collected accessions (landrace collections) in the West and Kellam Wallaga zones of Oromia, Ethiopia. The formal identification and voucher specimen of plant materials were undertook by Shambel Alemu at National herbarium laboratory of Addis Ababa University, Ethiopia. For comparison, improved variety, Nechsare provided by Pawe Agricultural Research Center and used as standard check in genotype by environment trials. The evaluated Mission grass (Pennisetum polystachion (L.) Schult) genotypes are listed in Table 2.

Table 2.

Descriptions of the mission grass genotypes used in the study

No.	Genotypes	Sources
1	NG-0057	Landrace collection
2	NG-0090	Landrace collection
3	NG-0112	Landrace collection
4	NG-0121	Landrace collection
5	Nechsare (standard check)	Pawe Agricultural Research Center

High-yielding genotypes selected from the preliminary yield trial; NG-0057, NG-0090, NG-0112, and NG-0121 along with a standard check (Nechsare) were evaluated in a two-year field study (2021–2022) using randomized complete block design (RCBD) with three replications.

Experimental procedures and field management

Fifteen plots, each observation plot measured 3 m in length and 1.8 m in width (5.4 m² area), consisting of six rows were established in a single field for soil uniformity. The experimental sites were cleared, plowed, and labeled before planting. Mission grass (Pennisetum polystachion (L.) Schult) seeds were sown in rows spaced 0.3 m apart. All plots received 100 kgha^− 1 of NPS fertilizer at the planting time and 50 kgha^− 1urea at split form (1/3 and 2/3 of the recommended rates applied during sowing period and afterward, respectively) Routine field management and agronomic practices were applied uniformly throughout the study.

Data collection and measurements

Data were recorded and harvested from two central rows to reduce border effect and provide representative samples for each replication. In this study data were collected on the following traits; heading, maturity, plant height, number of tiller and leaf per plant, leaf to stem ration. 50% heading (flowering) of the grass was recorded, while maturity was assessed at 90% physiological maturity. Plant height was measured from the base to the tip of heading using stick meter; the number of tillers and leaves were counted per plant from averaged five randomly selected plants per plot. The leaf to stem ratio was determined by cutting plants from randomly selected inner rows, separated into leaves and stems, dried and weighed. For herbage dry matter yield, fresh forage weight was harvested from two middle rows and immediately weighted in the field by using sensitive field balance scale. Then the weight of fresh samples were taken and oven dried at 65 °C for 72 h and the dried sample weight was recorded to calculate the dry matter yield using the following formula as described by Tarawali [14].

Where; DMY = dry matter yield, TFW = total fresh weight in kg, DWSs = dry weight of the subsample in grams, FWSs = fresh weight of the subsample in grams, 10 = is a constant for a conversion of yields in kgm^-2 to tha^-1.

Seed yield was harvested at optimum seed harvesting time from the inner rows and seed yield (tha^-1) calculated using the following formula;

Also. Percentage of yield advantage of the candidate genotypes were calculated based the following formula;

Laboratory analysis

For chemical analysis a 300 g subsample was then oven-dried at 65 °C for 72 h and ground using Wiley mill to pass through a 1 mm sieve screen. A milled composite sample per genotype was used for chemical analysis. Dry matter (DM), ash, and crude protein (CP) contents were determined following AOAC procedures [15]. The DM contend was determined by oven drying sample at 65 °C for 72 h and ash content was determined by complete burning of the feed samples in a muffle furnace at 600 °C overnight. The total nitrogen (N) was determined by the Kjeldhal procedure and CP was calculated as N * 6.25 factor. Fiber fractions neutral detergent fiber (NDF), acid detergent fiber (ADF) and acid detergent lignin (ADL) were analyzed using the detergent extraction method/Van Soest method [16].

In vitro dry matter digestibility (IVDMD) was analyzed using two stage rumen inoculates pepsin method of Tilley and Terry [17]. The rumen liquor was taken from rumen fistulated steers at the morning before animals were offered the feed, and then transported to the laboratory using thermos flask. A sample about 0.5 g was incubated with 30 ml of rumen liquor in 100 ml test tube in water bath at 39 °C for 48 h for microbial digestion, followed another 48 h for enzyme digestion with acid pepsin solution for IVDMD. Then the IVDMD was calculated as the formula described by Jeans and Yolade [18];

The chemical composition and IVDMD analysis were undertaken at Holeta Agricultural Research center animal feed analytical laboratory. Disease data were recorded at 1 to 5 scale scores.

Data analysis

Data from individual and combined environments across years were analyzed and analysis of variance (ANOVA) was performed using SAS 9.3 software. In this study, the genotypes were considered fixed variables, while the environments were treated as random. Different assumptions were stated before the combined analysis of variance (ANOVA) across environments performed, such as assumption of normality test was assessed separately for each environment and homogeneity of the variances was tested using Bartlett’s test. For the individual ANOVA analysis the following linear model was used [19];

Where Yij = yield of genotype i in block j; µ = grand mean; Gi = effect of genotype i; Bj = effect of block j; ℇij = error effect.

For the combined analysis across environments, the following linear model was used;

Where Yijk = yield and yield related traits of Mission grass genotype i in block k and environment j; µ = grand mean of the experiment; Gi = effect of genotype i; Ej = effect of environment j; (GE)ij = the interaction effect of genotype i with environment j; Bkj = effect of block k in environment j; ℇijk = error effect.

To assess the stability of the genotypes across environments, further statistical analysis was conducted using GenStat statistical package [20], such as Additive Main Effects and Multiplicative Interaction (AMMI) model, AMMI stability value and genotype selection index. AMMI model accounts for multiplicative interaction effects between genotypes and environments and these interactions capture how specific genotypes perform differently across various environments [21].

AMMI stability value

AMMI stability value (ASV) quantifies the stability of genotypes based on interaction principal components in the AMMI model and was calculated as the distance from the coordinate point to the origin in a two-dimensional plot of IPCA1 scores against IPCA2 scores, following the method proposed by Purchase et al. [22].

The larger the IPCA score, either negative or positive, the more specifically adapted a genotype is to certain environments. The smaller IPCA score indicates a more stable genotype across environments.

Genotype selection index

Genotype selection index (GSI) was computed for each genotype, based on the rank of mean yield across environments and the rank of AMMI stability value as suggested by Farshadfar [23]. Based on this criterion, a genotype with the lowest value of GSI would be more stable.

Where, RY = Rank of yield, RASV = Rank of AMMI Stability Value.

GGE biplot model

GGE biplot analysis was employed, identifying key factors genotype (G) and genotype-by-environment (G×E) interaction that influence genotype evaluation and G×E interaction variation [24]. The GGE biplots were further used to interpret G×E interactions, helping to identify stable and adaptive genotypes [25].

Results and discussion

Analysis of variance

Combined analysis of variance (ANOVA) for agronomic traits and yield of Mission grass genotypes, evaluated across three environments over two years, revealed significant environmental effects on all measured traits (Table 3). Genotypic variation was significant for all yield and yield-related traits except number of leaf per plant and leaf-to-stem ratio. Additionally, genotype-by-environment (G×E) interactions significantly influenced herbage dry matter yield, seed yield, and all agronomic traits except leaf-to-stem ratio. The significant environmental effects on yield and yield-related traits suggest that edaphic, biotic, and abiotic factors strongly influenced genotypic performance. Similar environmental impacts on forage crops have been reported in previous studies [26, 27]. The observed variation among Mission grass genotypes aligns with findings by Lawal et al. [26], who noted significant differences in agronomic traits due to species variability. The presence of significant G×E interactions further underscores the differential adaptation of genotypes across environments, consistent with reports by Kebede et al. [28] in oats and Lawal et al. [26] in forage grasses. These findings highlight the importance of multi-environment evaluations in selecting stable and high-performing genotypes for diverse growing conditions.

Table 3.

Combined ANOVA for yields and yield components of mission grass genotypes during 2021–2022

Source of variation		Mean square
	DF	DH	DM	PH	NTPP	NLPP	LSR	DMY	SY
Replication	2	8.21NS	1.54NS	261.07NS	3.57NS	0.04NS	0.03NS	1.74 NS	2443.81NS
Genotypes (Gen)	4	214.05**	483.68**	244.40*	84.88**	0.52NS	0.13NS	21.34**	69480.11**
Environment (Env’t)	2	699.91**	2224.67**	16391.71**	361.55**	12.49**	0.48**	4.56*	578382.24**
Year (Yr)	1	154.71**	40**	57.76NS	189.05**	6.29**	0.56**	171.64**	169885.15**
Gen*Env’t	8	13.35**	25.73**	288.64*	21.52*	1.79**	0.06NS	3.15*	22572.98*
Error	58	2.39	3.62	93.82	8.67	0.62	0.07	1.25	8917.29

 DF Degree of freedom, DH Days of 50% heading, DM Days of maturity, PH Plant height, NTPP number of tillers per plant, NLPP number of leaves per plant, LSR leaf to stem ration, DMY Dry matter yield, SY Seed yield, Gen Genotype, Env’t Environment, Yr Year, NS Non-significant

*, **significant at 5% and 1% probability level, respectively

Agronomic traits

The combined analysis of mean agronomic traits among Mission grass genotypes revealed significant differences (p < 0.05) for most traits, except for the number of tiller per plant, number of leaves per plant, and leaf-to-stem ratio (Table 4). The genotype NG-0121 exhibited significantly takes longer days of 50% flowering/heading (133.39 days) and maturity (178.38 days) compared to other genotypes, followed by NG-0057. In contrast, Nechsare (standard check) and NG-0112 showed earlier days of flowering and maturity. Additionally, NG-0121 recorded greater plant height, number of tillers per plant, number of leaves per plant, and stem-to-leaf ratios, while NG-0112 had the lowest values for these traits. The observed variations in agronomic traits among Mission grass genotypes suggest genetic diversity in growth and developmental patterns. The delayed flowering and maturity in NG-0121 may be advantageous for prolonged vegetative growth, potentially enhancing biomass yield, whereas early-flowering genotypes like Nechsare and NG-0112 could be suitable for short-duration forage production. The superior plant height and tillering ability of NG-0121 indicate its potential for higher forage productivity, consistent with findings by Abate and Fikere [29], Abuy et al. [30] and Tessema and Getnet [31], who also reported genotypic differences in plant height and days to flowering in forage crops. These results illustrate the importance of genotype selection based on targeted agronomic traits for improved forage production.

Table 4.

Mean performance of agronomic characteristics of mission grass genotypes tested across locations during 2021–2022

Genotypes	DH	PH (cm)	NTPP	NLPP	LSR	DM
NG-0057	129.44b	142.32ab	18.83a	7.38	1.16	173.00b
NG-0090	124.50d	146.76a	16.40b	7.64	1.05	166.83c
NG-0112	127.05c	136.89b	14.87b	7.67	0.99	166.55c
NG-0121	133.39a	144.33a	19.94a	7.84	1.17	178.38a 167.33c
Nechsare (check)	126.16c	141.27ab	15.58b	7.54	1.01
Mean	128.11	142.31	17.12	7.62	1.07	170.42
CV (%)	1.21	6.80	17.20	10.38	25.00	1.12
LSD (%)	1.03	6.46	1.97	0.53	0.18	1.27
p-value	< 0.0001	< 0.0001	0.0442	0.147	0.7642	< 0.0001

DH Days of 50% heading, PH Plant height, NTPP Number of tiller per plant, NLPP Number of leaves per plant, LSR Leaf to stem ratio, DM Days of maturity, cm centimeter

^a−dmeans in the column with the same letter are not significantly different

Dry matter yield and seed yield

The mean dry matter (DM) yield of Mission grass genotypes evaluated at Haro Sabu and Ayira locations during the 2021 and 2022 growing seasons showed significant variation, but didn’t varied at Kombo location (Table 5). The highest DM yield was recorded by NG-0121 (8.79 tha^-1), followed by NG-0057 (7.35 tha^-1), while the standard check (Nechsare) had the lowest yield (6.11 tha^-1). Among the tested genotypes, three genotypes outperformed the standard check by more than 10%, with NG-0121 and NG-0057 exhibiting yield advantages of 43.86% and 20.29%, respectively, over Nechsare. The significant variation in DM yield among genotypes suggests genetic differences and genotype-by-environment interactions. The superior performance of NG-0121 and NG-0057 indicates better adaptation to the test environments compared to the standard check. Location-specific effects played a crucial role in DM yield, leading to differential genotype responses, consistent with findings by Tulu et al. [32] in oat genotypes. The substantial yield advantage of NG-0121 (43.86%) and NG-0057 (20.29%) over Nechsare highlights their potential as high-yielding forage genotypes.

Table 5.

Mean DM yield (tha− 1) of mission grass genotypes in the three environments during 2021–2022

Genotypes	Haro Sabu		Kombo		Ayira		Mean	Yield adv. %
	2021	2022	2021	2022	2021	2022
NG-0057	7.91b	6.12b	9.48	6.78	8.36ab	5.45ab	7.35b	20.29
NG-0090	8.01b	6.27b	9.74	5.59	7.65b	6.75a	7.33b	19.97
NG-0112	6.07b	4.60b	8.59	5.85	8.21ab	3.84c	6.19c	1.31
NG-0121	11.67a	8.44a	10.21	6.48	10.19a	5.73ab	8.79a	43.86
Nechsare (check)	7.80bc	5.05b	7.86	4.67	6.30b	5.00bc	6.11c	-
Mean	8.29	6.09	9.18	5.87	8.14	5.35	7.15
CV (%)	11.21	17.13	18.17	19.04	15.04	13.50	15.68
LSD (%)	1.75	1.96	3.14	2.11	2.31	1.36	0.75
p-value	0.001	0.015	0.4793	0.2581	0.0471	0.0124	0.0281

CV coefficient of variation, LSD least significant difference

^a−cmeans in the column with the same letter are not significantly different

Seed yield varied significantly among genotypes at both Haro Sabu and Ayira locations, though didn’t varied at Kombo in both years (Table 6). The combined mean seed yield showed that NG-0057 produced the highest seed yield (760.74 kgha^-1), followed by NG-0121 (661.43 kgha^-1). The standard check (Nechsare) recorded a lower seed yield (592.83 kgha^-1), which was statistically comparable to NG-0112 (636.63 kgha^-1) and NG-0090 (644.71 kgha^-1). The observed seed yield differences likely showed from genetic factors and environmental conditions. NG-0057’s superior seed yield suggests strong genetic potential, while the comparable performance of NG-0112 and NG-0090 to the standard check indicates moderate adaptability. These findings align with Kebede et al. [28], who reported similar genetic and environmental influences on oat grain yields. The high seed yield of NG-0057 and NG-0121 reinforces their suitability for both forage and seed production, warranting further evaluation for commercial release.

Table 6.

Mean seed yield (kgha^− 1) of mission grass genotypes tasted at three environments during 2021–2022

Genotypes	Haro Sabu		Kombo		Ayira		Combined Mean
	2021	2022	2021	2022	2021	2022
NG-0057	571.93ab	848.70a	1153.10	694.26	703.67a	592.78a	760.74a
NG-0090	287.67c	848.80a	929.10	595.28	685.13a	522.22ab	644.71bc
NG-0112	679.10a	550.37b	935.20	690.37	592.57ab	372.13c	636.63bc
NG-0121	614.03ab	788.24a	890.10	623.98	555.50ab	496.76ab	661.43b
Nechsare (check)	446.33bc	526.85b	1015.80	618.24	481.43b	468.33bc	592.83c
Mean	519.81	712.59	984.67	644.43	603.66	490.44	659.27
CV (%)	20.57	9.85	13.24	14.99	13.56	12.69	14.32
LSD0.05 (%)	201.36	132.09	245.5	181.99	154.11	117.21	63.01
p-value	0.0135	0.007	0.1983	0.642	0.0409	0.0462	< 0.0001

^a−dmeans in the column with the same letter are not significantly different

Chemical composition and digestibility

The chemical composition and digestibility of Mission grass was varied among the tested genotypes (Table 7). Among the evaluated genotypes, NG-0090 exhibited the highest crude protein (CP) content (12.22%), closely followed by Nechsare (12.08%) and NG-0057 (11.67%), while NG-0121 had the lowest CP content (10.58%). These differences might be due to genotypes phonological differences reached for forage harvesting stage, as age of harvesting increases the CP content was reduced. The CP values the studied genotypes were higher than those reported for other grass species in different environments [33]. Fiber content followed an inverse trend, with NG-0121 recording the highest neutral detergent fiber (NDF, 81.61%) and acid detergent fiber (ADF, 55.03%), followed by NG-0112 (79.12% NDF) and NG-0057 (51.05% ADF). Conversely, NG-0090 had the lowest fiber content. The higher NDF and ADF values in this study compared to typical tropical grasses [34, 35] suggest that these genotypes may have greater cell wall content, potentially reducing digestibility. Despite its lower CP content, NG-0121 produced the highest CP yield, followed by NG-0057, due to higher dry matter yield was obtained, whereas Nechsare had the lowest CP yield. While NG-0090 had the highest CP content, NG-0121 demonstrated the highest CP yield, indicating that CP yield is a more reliable indicator of forage quality and productivity than CP content alone. This aligns with previous findings that higher CP yield reflects superior genotype performance in terms of both quantity and nutritional value.

Table 7.

Chemical composition, in vitro dry matter digestibility and crude protein yield of the tested mission grass genotypes

Genotypes	Chemical composition and in vitro dry matter digestibility (%)							CPY (t/ha)
	DM	Ash	CP	NDF	ADF	ADL	IVDMD
NG-0057	89.49	6.98	11.67	76.7	51.05	7.99	50.73	0.86
NG-0090	90.1	4.23	12.22	76.38	49.35	6.81	45.37	0.89
NG-0112	90.42	4.16	11.42	79.12	50.53	7.3	51.71	0.71
NG-0121	89.91	4.92	10.58	81.61	55.03	7.67	51.61	0.93
Nechsare(check)	89.85	6.89	12.08	77.23	50.57	7.25	42.54	0.74

DM dry matter, CP crude protein, NDF neutral detergent fiber, ADF acid detergent fiber, ADL acid detergent lignin, IVDMD in vitro dry matter digestibility, CPY crude protein yield

For in vitro dry matter digestibility (IVDMD), NG-0112 showed the highest value (51.71%), followed by NG-0121 (51.61%) and NG-0057 (50.73%), whereas Nechsare (standard check) had the lowest digestibility (42.54%). NG-0112 and NG-0121 had the highest IVDMD despite their high fiber content, possibly due to differences in lignin composition or other cell wall characteristics that enhance degradability. In contrast, Nechsare, despite its high CP content, had the lowest digestibility, suggesting that factors beyond CP, such as fiber composition and lignin content, play a critical role in forage digestibility. The inverse relationship between fiber content and digestibility was evident, as genotypes with higher NDF exhibited lower digestibility, consistent with findings by Amiri [36]. These findings highlight the importance of selecting genotypes based on both CP yield and digestibility for improved forage quality and livestock performance.

Disease reaction

The genotypes exhibited varying reactions to disease infections. The genotypes NG-0121 and NG-0057 demonstrate superior tolerance compared to other genotypes and the standard check against leaf rust, grey leaf spot and anthracnose (Fig. 1). The superior disease tolerance observed in genotypes NG-0121 and NG-0057 suggests their potential as resistant sources against leaf rust, grey leaf spot, and anthracnose. The enhanced tolerance of these genotypes could be attributed to inherent resistance mechanisms, such as improved immune responses or structural barriers against pathogen invasion.

Fig. 1.

Response of Mission grass genotypes for leaf rust, anthracnose and grey leaf spot

Stability analysis

Additive main effect and multiplicative interaction analysis

The Additive Main Effects and Multiplicative Interaction (AMMI) analysis was a powerful tool for visualizing G×E interaction in Mission grass genotypes against the standard check (Nechsare) across six locations. The stability analysis for genotype-by-environment (G×E) interaction revealed significant main effects of genotypes and environments, as well as a significant G×E interaction effect on herbage dry matter (DM) yield (Table 8).Variance partitioning indicated that the environment contributed the largest proportion (58.20%) of the total DM yield variation, followed by genotype (26.82%) and G×E interaction (14.98%). The first two interaction principal component axes (IPCA1 and IPCA2) of the AMMI model captured 62.23% and 37.77% of the interaction sum of squares, respectively.

Table 8.

ANOVA for AMMI model for DMY of mission grass genotypes tested under different location during 2021–2022

Source	df	SS	MS	% SS explained
Total	89	394.9	4.437
Treatments	29	318.4	10.979
Genotypes	4	85.4	21.34**	26.82
Environments	5	185.3	37.064**	58.20
Block	12	12.5	1.04
Interactions	20	47.7	2.386*	14.98
IPCA1	8	28.5	3.567*	62.23
IPCA2	6	17.3	2.89	37.77
Residuals	6	1.8	0.307
Error	48	64	1.333

df degree of freedom, SS sum of square, MS mean square

The substantial environmental contribution to DM yield variation suggests high variability across testing locations, indicating that diverse environmental conditions were the primary driver of fodder yield differences. This finding aligns with Adjebeng-Danquah et al. [37], who reported that the environment contributed the largest share of the treatment sum of squares, followed by genotype and G×E interaction. Similar results have been observed in previous studies on Napier grass [38] and oat genotypes [39], where environmental factors predominantly influenced DM yield variation. The dominant role of the environment suggests that biotic and abiotic factors were key determinants of yield differences among genotypes. Genotypic variation also significantly affected herbage DM yield across environments, while the significant G×E interaction indicated genetic variability and unstable genotypic responses to environmental changes. The high explanatory power of IPCA1 and IPCA2 (62.23% and 37.77%, respectively) demonstrates that the AMMI model effectively captured G×E interaction patterns, facilitating a better understanding of genotype stability and adaptation across diverse environments.

AMMI stability value

Genotypes with lower AMMI Stability Values (ASV) exhibited greater stability, while higher ASV values were associated with instability in dry matter yield across environments [22]. In this study, NG-0057 and Nechsare were the most stable genotypes, with ASV values of 0.15 and 0.28, respectively (Table 9). In contrast, NG-0112, with an ASV of 1.27, showed the lowest stability in dry matter yield. The findings on AMMI Stability Value (ASV) align with previous studies by Tadese [40] and Wodebo et al. [41], who also reported that genotypes with lower ASV values were more stable. However, stability alone does not guarantee high yield, as some stable genotypes may have lower mean dry matter yield. This suggests that relying solely on ASV for genotype selection may be insufficient.

Table 9.

AMMI stability value, genotype selection index and yield rank of the tested Mission grass genotypes

Genotype	Gm	Rank	IPCAg[1]	IPCAg[2]	ASV	ASV Rank	GSI
Nechsare (check)	6.11	5	-0.24539	0.68663	0.79	2	7
NG-0057	7.35	2	0.46059	-0.1912	0.78	1	3
NG-0090	7.33	3	0.29022	0.91879	1.04	3	6
NG-0112	6.19	4	0.88738	-0.86827	1.70	4	8
NG-0121	8.79	1	-1.39281	-0.54594	2.36	5	6

Gm Grand mean, ASV AMMI stability value, GSI Genotype selection index

Genotype selection index

The Genotype Selection Index (GSI), which combines mean yield and ASV, was used to identify genotypes with both high yield and stability. A lower GSI value indicates better stability and yield performance. NG-0057 had the second ranked dry matter yield and greater stability (GSI = 3), while NG-0121 had the highest dry matter yield and relative stability (GSI = 6). NG-0112 and Nechsare was the least stable. The Genotype Selection Index (GSI) provides a more robust approach by integrating both yield performance and stability. This method ensures that selected genotypes are not only stable but also high-yielding, as demonstrated by NG-0057 and NG-0121, which had the lowest GSI values. This result is consistent with Mohammadi et al. [42], who emphasized the importance of combining yield and stability in genotype evaluation. The use of GSI supports the argument by Mahmodi et al. [43] that stability indices should incorporate both yield and adaptability for effective genotype selection. Thus, integrating ASV and mean yield into a single index, such as GSI, enhances selection efficiency, particularly in variable environments.

Genotypes and genotypes by environment interaction (GGE) biplot analysis

The polygon view of the GGE biplot (Fig. 2 (a & b) was used to identify top-performing genotypes and assess their interaction patterns across different environments. The “which-won-where” pattern effectively identifies the best performing genotypes in each environment or group of environments, referred to as mega environments. The polygon views of the GGE biplot (Fig. 2 (a)) show which genotypes performed best for dry matter yield in each mega environment. The “which-won-where” pattern divided the polygon into four sectors using perpendicular lines from the origin. The vertex genotypes NG-0121, NG-0112, and the check variety (Nechsare) exhibited higher genotype-by-environment interaction (G×E) variance indicating their differential responses to environmental conditions, while the remaining genotypes were positioned inside the polygon. NG-0121 was best for dry matter yield and is stable in Kombo, Haro Sabu and Ayira, while NG-0112 and check variety (Nechsare) were unstable due to located farther from the origin, suggesting greater sensitivity to environmental changes and specific adaptation.

Fig. 2.

GGE biplots shows; (a) “which-won-where” pattern; (b) discriminativeness and representativeness of test environments for Mission grass genotypes

The GGE biplot of the first (PC1) and second (PC2) principal components explained 91.09% of the total variation (PC1 = 77.32%, PC2 = 13.78%), confirming its suitability for assessing interrelationships across environments (Fig. 2b). The length of environmental vectors indicated discriminative power, with Haro Sabu-2021 being the most discriminating environment, followed by Haro Sabu-2022 and Ayira-2021. Conversely, Kombo-2022 and Ayira-2022 showed weak or no discrimination among genotypes. The discriminative ability of test environments varied, with Haro Sabu-2021 emerging as the most effective in differentiating genotypes, consistent with findings by Yan et al. [11]. This suggests that Haro Sabu is an optimal testing site for selecting superior genotypes due to its strong representativeness and discriminative power.

The GGE biplot was an insightful application for evaluating genotypes relative to and ideal genotypes. Based on the GGE biplot methodology, genotypes closest to the center of concentric circles were considered ideal, balancing stability and mean yield. According to different authors including Artur et al. [44] and Fantaw [45], had defined an ideal genotype as having both high mean performance and consistent stability across environments. According to the GGE biplot graph (Fig. 3), genotype NG-0121 was positioned within the first concentric circle, indicating that it was the ideal genotypes, exhibiting both high stability and superior dry matter yield. It was followed by NG-0057, which was also desirable due to their high mean forage yield and relatively stable across the environments. The identified result of NG-0121 as ideal genotypes aligns with previous studies [41, 46], which reported that ideal genotypes combine high yield potential with stability across diverse environments. These genotypes are recommended for further breeding and production due to their consistent performance. Among the test environments, Haro Sabu was the most representative and discriminating, making it the most desirable testing site for evaluating Mission grass (Pennisetum polystachion (L.) Schult) genotypes.

Fig. 3.

GGE-biplot based on genotype and environment focused scaling for comparison of genotype and environment for DMY

Correlation analysis

The relationships among agronomic characteristics, chemical compositions, and yields of Mission grass genotypes are presented in Table 10. The results reveal that agronomic traits were positively correlated with one another and forage yields. Crude protein (CP) content was positively correlated with leaf to stem ratio (r = 0.558) and in-vitro dry matter (DM) digestibility (r = 0.770) but negatively related with dry matter yield (r= -0.659), crude protein yield (r= -0.337), fiber contents and most agronomic parameters. Dry matter yield was a positive and statistically significant correlation (p < 0.05) between number of tillers per plant and crude protein yield. Dry matter yield had a significant positive correlation with crude protein yield (r = 0.929) but it was negatively correlated with ash (r= -0.170) and crude protein content (r= -0.659). Moreover, in vitro DM digestibility was positively correlated with crude protein (r = 0.770), leaf to stem ratio (r = 0.481), yields and other quality and morphological parameters and negatively correlated with neutral detergent fiber (r= -0.594).

Table 10.

Correlation coefficients among agronomic traits, chemical composition and yields of mission grass genotypes

Variables	PH	NTPP	NLPP	LSR	DMY	CPY	Ash	CP	NDF	ADF	ADL	IVDMD
PH	1.00
NTPP	0.509	1.00
NLPP	0.137	0.140	1.00
LSR	0.491	0.989**	0.017	1.00
DMY	0.646	0.907*	0.475	0.864	1.00
CPY	0.856	0.838	0.262	0.823	0.929*	1.00
Ash	-0.048	0.221	-0.724	0.267	-0.170	-0.103	1.00
CP	0.123	-0.626	-0.626	0.558	-0.659	-0.337	0.223	1.00
NDF	-0.162	0.412	0.810	0.309	0.535	0.195	-0.365	-0.943*	1.00
ADF	0.118	0.761	0.584	0.676	0.760	0.486	-0.003	-0.926*	0.878*	1.00
ADL	-0.208	0.682	-0.286	0.722	0.361	0.203	0.555	-0.561	0.298	0.564	1.00
IVDMD	-0.304	0.462	0.253	0.481	0.443	0.211	0.305	0.770	-0.594	0.522	0.610	1.00

PH plant height, NTPP number of tiller per plant, NLPP number of leaves per plant, LSR leaf to stem ratio, DMY dry matter yield, CPY crude protein yield, CP crude protein, NDF neutral detergent fiber, ADF acid detergent fiber, ADL acid detergent lignin, IVDMD in vitro dry matter digestibility

*and **: significant at p < 0.05 and p < 0.01, respectively

The observed positive correlation between agronomic traits and yields in present study is align findings of Imran et al. [47], and Mekonnen and Mekuriaw [48], reported that growth parameters are critical in increasing forage biomass yield. The negative correlation of dry matter yield with forage quality parameters such as ash and CP content in the current study agreed with the finding of Birilie [49]. Furthermore, in this study, the crude protein and in vitro DM digestibility were negatively correlated with neutral detergent fiber (NDF), in fact, this is biologically expected, as fiber fractions are highly lignified, which reduces digestibility, as indicated by Wilson [50] and similar results are reported by Enoh et al. [51] and Tessema et al. [52] in Urochloa grass cultivars.

Conclusion

Significant trait variations and genotype-by-environment interactions were observed among Mission grass (genotypes across six environments, indicating differential performance across diverse locations. The substantial environmental effects further confirmed the variability of the test sites. These genotype-by-environment interactions complicated the selection of universally superior genotypes due to their inconsistent performance under varying conditions. Stability parameters including IPC1, IPC2, AMMI stability value, genotype selection index, and GGE biplot identified NG-0121 was high yielder and stable genotypes for both dry matter and crude protein yields, followed by NG-0057. Therefore, validating and releasing these relatively stable and high-performing genotypes is essential for improving forage varieties. Given their superior performance, NG-0121 and NG-0057 are recommended for cultivation and potential release as varieties in the low-midlands of Western Oromia and other regions with similar agro-ecologies in Ethiopia.

Authors’ contributions

Yerosan Wekgari: Writing – review & editing, Writing – original draft, Supervision, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Fikre Dereba: Writing – review & editing, Investigation, Conceptualization.

Funding

The authors received no specific funding for this research work.

Data availability

All data are provided within the manuscript.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.