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34 

  

     MULTIDISCIPLINARY SCIENTIFIC RESEARCH 

 
        BJMSR VOL 8 NO 1 (2023)  P-ISSN 2687-850X  E-ISSN 2687-8518 

 
        Available online at https://www.cribfb.com 

     Journal homepage: https://www.cribfb.com/journal/index.php/BJMSR 

                                                                                                                                                                                                    Published by CRIBFB, USA 

                                                                                                                                            

EVALUATION OF GENOTYPES WITH ENVIRONMENTAL 

INTERACTIONS OF LABLAB (PURPUREUS L.) AND IT IS DRY 

MATTER YIELDS STABILITY IN THE MIDLAND OF GUJI ZONE, 

SOUTHERN OROMIA, ETHIOPIA              

          
 Teshale Jabessa (a)1      Getachew Tesfaye (b)     Ketema Bekele (c)   

 

(a) Animal Feed Researcher, Oromia Agricultural Research Institute, Ethiopia; E-mail: teshalejabessa@gmail.com 
(b) Animal Feed Researcher, Oromia Agricultural Research Institute, Ethiopia; E-mail: getchtesfaye48@gmail.com 
(c) Animal Feed Researcher, Oromia Agricultural Research Institute, Ethiopia; E-mail: ketamabekele@gmail.com 
 

 
A R T I C L E I N F O 

 
 

Article History: 
 

Received: 24th August 2023 

Revised: 29th November 2023 

Accepted: 22nd December 2023 

Published: 31st December 2023 

 
Keywords: 

 

Dry Matter, Environment, Interactions, 
Lablab, Stability 

 

 

 
JEL Classification Codes: 

 

F25, Q35, W22, M83 

 
 

 

 

 

 

  

 
A B S T R A C T 
 

Animal feed is one of the main challenges facing livestock producers, due to inadequate nutrition, particularly 

during the dry season. The aim of this study was to identify Lablab genotypes performance in different midlands 

areas of Guji zones. A 3mx2m plot was used to seed twelve genotypes of Lablab purpureus, which were obtained 

from the International Livestock Research Institute Gene Bank, and a tick registered variety from Bako 

Agricultural Research Centre. During the main cropping rainy season in 2021-2022, three locations Dufa, 

Gobicha, and Kiltu sorsa, Adola subsite, and on farms in two (2) consecutive years, respectively were studied 

using randomized complete block designs (RCBD) with three replications. Information was gathered regarding 

the establishment, duration of various physiological stages, dry matter yield of fodder, chemical compositions, 

and additional relevant factors. AMMI and the SAS statistical analysis programmer, version (2002), were used 

to perform an analysis of variance on the gathered data. The list significant difference test was used to compare 

the means. The results of the AMMI analysis of variance for forage dry matter yield showed that there were 

substantial (P<0.01) variations in genotype and environment, but not in the effects of the G x E interaction. 

Both the representative testing site and the testing conditions (Adola woyu and Kiltu sorsa) were quite good at 

differentiating genotypes. The combined analysis of the data revealed that  non-significant (P>0.05) differences 

for plant height and thousand seed weight, but significant (P ≤ 0.05) differences for days to flowering, days to 

maturity, number of branches, leaf to steam ratio, number of pods, and number of seeds across the tested 

environments. The results showed that, out of all the examined locations, G-11620 (15.43 t/ha) and G-14486 

(11.12 t/ha) had the highest forage dry matter production. It was observed that the leaf to steam ratio was higher 

in both G-11486 and G-11620.  All chemical compositions across the tested genotypes were found to be 

significantly different (p ≤ 0.05) among parameters, with the exception of DOMD and IVDMD, which did not 

showed significant (p >0.05) variations among genotypes. The recorded CP content ranged from 21.15% for 

G-14486 to 23.50% for G-11620, with the lowest value coming from typical cheek Gabis 10.8%. The highest 

and the lowest NDF were recorded from G-11620 (11.2%) and Gabis (22.23%) respectively. Generally the 

mean performance, yield and stability of the G-11620 and G-14486 were high and stable across the tested 

locations. Therefore, genotypes (G-14486 and G-11620) were promoted to variety verification for further 

evaluation and possible for release. 
 

 

© 2023 by the authors. Licensee CRIBFB, U.S.A. This open-access article is distributed under the 
terms and conditions of the Creative Commons Attribution (CC BY) license 
(http://creativecommons.org/licenses/by/4.0/).  

            

 

INTRODUCTION 

Animal feed is one of livestock producers' most significant issues (Mohamed, 2017). Like other tropical countries, Ethiopia's 

smallholder crop-livestock farmers face substantial challenges due to inadequate nutrition, particularly during the dry season 

when pastures and crop leftovers are scarce and of low nutritional value (Tolera et al., 2000). Introducing improved types 

of highly productive forage plants with good dietary quality is crucial given the enormous activities at fodder production 

(Tekalign, 2014).  

Among cultivated plants in the Leguminosae family, Lablab (Lablab purpureus (L) Sweet) is a primarily self-fertile 

herbaceous forage crop with chromosomal number 2n 22 (Kshirsagar et al., 2018). Due to its higher forage yields than 

cowpea and adaptability to various agroecologies, it has tremendous potential as a species of forage crop (Adebisi et al., 

2004). Lack of agronomic practices, genetic erosion, limited research focus, absence of improved varieties, poor 

                                                      
1Corresponding author: ORCID ID: 0000-0003-2936-2374 
© 2023 by the authors. Hosting by CRIBFB. Peer review under the responsibility of CRIBFB, U.S.A.  

https://doi.org/10.46281/bjmsr.v8i1.2169 

 
To cite this article: Jabessa, T., Tesfaye, G., & Bekele, K. (2023). EVALUATION OF GENOTYPES WITH ENVIRONMENTAL INTERACTIONS OF 

LABLAB (PURPUREUS L.) AND IT’S DRY MATTER YIELDS STABILITY IN THE MIDLAND OF GUJI ZONE, SOUTHERN OROMIA, 

ETHIOPIA. Bangladesh Journal of Multidisciplinary Scientific Research, 8(1), 34-43. https://doi.org/10.46281/bjmsr.v8i1.2169 

http://creativecommons.org/licenses/by/4.0/)
http://creativecommons.org/licenses/by/4.0/)
https://doi.org/10.46281/bjmsr.v8i1.2169
https://orcid.org/0000-0003-2936-2374
https://orcid.org/0009-0004-0265-3520
https://orcid.org/0009-0006-8168-1777


Jabessa et al., Bangladesh Journal of Multidisciplinary Scientific Research 8(1) (2023), 34-43 

 

35 

management practices and improvement, and variations in climate patterns have all contributed to the decline in lablab 

production patterns (Bhatt et al., 2019). Lablab is multipurpose fodder, and various plant parts, including seeds, young 

grains, green beans, leaves, and biscuits, can be eaten (Davari et al., 2018). 

Lablab purpureus forage is a short-lived, upright perennial herbaceous crop frequently planted annually (Kikafunda 

et al., 2004). It is recommended because of its high nutritional content, palatability, and strong forage production. 

Furthermore, Lablab purpureus is a good candidate for intercropping. It is shade-tolerant and has been cultivated in dry and 

semiarid environments because of its drought tolerance (Aganga & Tshwenyane, 2003).  

Nowadays, one of the main fodder crops used as green manure and leguminous is Lablab purpureus. Nevertheless, 

the forage yield is significantly lower than its potential because of several biophysical and socioeconomic limitations and a 

limited variety selection. Therefore, one of the most significant ways to reduce those production constraints is to cultivate 

forage crop varieties that are resistant to substantial biotic and abiotic stress and that improve adaptation to changing 

environments and in different agro-ecologies Therefore, the present study was initiated to estimate the magnitude of 

genotype, environment and genotype by environment interaction for forage yield and yield components of Lablab yield 

stability across different environments. 

 

LITERATURE REVIEW 

Lablab is a high-yielding forage legume which can be grazed, harvested for hay or silage, or used as a green manure and 

break crop in sub-tropical and tropical farming systems (Chakoma et al., 2016). It is commonly used as a supplementary 

feed (Tulu et al., 2018) for intercropping with cereal crops (Mpairwe et al., 2002). It is considered to have significant 

potential for the sustainable intensification of smallholder crop/livestock production systems (Miller et al., 2018). It tolerates 

acid soil conditions (Mugwira & Haque, 1993) and addresses soil fertility decline. Lablab is also used to control insect pests 

(Qureshi et al., 2016) and in ethno-veterinary medicine. Lablab forage yields range from 6-9 tonnes of dry matter (D.M.) 

per hectare. Forage has an average crude protein content of about 16% D.M., which can vary from 8-33% in sub-Saharan 

Africa, depending on local conditions and stage of harvest. The crude protein levels in the leaves range from 21-38%, in the 

stem 7–20%, and in the grain 20-28%. The digestibility of leaves ranges from 55-76% (Mudunuru et al., 2008). The latest 

research findings report the effectiveness of lablab bean extracts in impeding infections of viral diseases such as influenza 

and SARS-CoV-2, which has been described as a world pandemic (Liu et al., 2020). The crop also greatly ensures income 

security among smallholder farmers, especially in dryland and semi-dryland ecosystems (Rai et al., 2018). This demonstrates 

the necessity of improving its production and utilization. Lablab is a multipurpose crop. Several plant parts can be consumed 

for human consumption (Rana et al., 2021) and animal feed (Minde et al., 2021). 

Lablab is a drought-resilient crop with multiple benefits (Naeem et al., 2020). It is popularly regarded as a grain 

legume, vegetable, and fodder, rich in protein (comparable with soybean), nutrients, and vitamins (Minde et al., 2020). Its 

ability to thrive when rainfall resumes after drought has led to its greater resilience compared with other legumes such as 

common beans (Phaseolus vulgaris), soybeans (Glycine max), cowpeas (Vigna unguiculata), and pigeon peas (Cajanus 

cajan) (Miller et al., 2018). Phenotypic plasticity in plants refers to the changes in physiological responses that contribute 

to their adaptability to the new environment (Alpert & Simms, 2002). Morphologically, the mechanisms include glabrous 

and trailing stems, a vigorous extension of shoots, shifting of leaf inclinations to reduce sun rays, decrease in leaf sizes and 

structures, changes in chlorophyll contents and greenness of the crop, alterations in stomatal behaviour, and their distribution 

to control evapotranspiration as well as deep root penetration (2 m) to the soil (Chakoma et al., 2016) 

 

MATERIALS AND METHODS 

Description of the Study Locations 

The experiment was conducted at three locations (Adola et al., 2021), sorsa on the farm in the Guji zone of southern Oromia 

for two consecutive years. The areas under examination cover the sub-humid mid-altitude primary crop-growing region 

with an altitude range from 1450 to 1900 meters above sea level. The area's first and most significant rainy season falls 

between April and August, while the second rainy season falls between September and November. The area has bimodal 

rainfall. The district receives 1084 mm of rainfall annually and is divided into three agroecologies: lowland (60%), midland 

(29%) and highland (11%). The research site's average annual lowest temperature is 15.93 °C, while its average yearly 

maximum temperature is 9.89 °C. The primary soil types of the area are basaltic soil (Nitisols) and Orthic Aerosols (Etefa 

& Dibaba, 2011). 

 

Approaches and Design of Experiments 

Twelve Lablab genotypes, including standard checks (Gabisa-17 and Beresa-55), were included in the genetic materials and 

examined at six different locations throughout the study period between two years (2021 to 2022). A randomized complete 

block (RCBD) with three replications was used in each location. With a 2 m length, 1.8 m width, and 30 cm inter-row 

spacing, each genotype was seeded in six rows. When planting, 20 kg ha-1 of seeds and 100 NPS kg ha-1 of fertilizer were 

applied. 

 

Sources of Planting Materials 

The International Livestock Research Institute (ILRI) first provided the planting materials used in this investigation. Based 

on their performance in terms of herbage yield and other agronomic parameters, the Lablab genotypes aside from the check 

(Gabisa-17 and Beresa-55) assessed in the presented study were chosen from those previously adapted to the environment. 

 

 



Jabessa et al., Bangladesh Journal of Multidisciplinary Scientific Research 8(1) (2023), 34-43 

 

36 

Methods of Data Collections 

The agronomic data like date of 50% flowering, Number of branches per plant, Number of leaves per plant, Leaf to stem 

ratio, plant height (cm), dry matter yield (t/ha), number of pods per plant, number of seeds per pod, seed yield (kg/ha) and 

thousand seed weight (g) was carefully collected. Forage sampling was collected at the 50% flowering stage, and seed 

sampling was conducted at the maturity stage of the plant. In all plots, sampling was done from the middle four rows, 

excluding the border rows. 

 

Biomass Yield Determination  

A delicate balance was used in the field to weigh the herbage yield, which was harvested 10 cm above the ground. Fresh 

subsamples will be independently obtained from each plot, weighed, and chopped into pieces ranging from 2 to 5 cm to 

determine the dry matter content. The weighed fresh sub-samples (FWss) were oven-dried at 600C for 72 hours and re-

weighed (DWss) to estimate dry matter yield. 

 

The dry matter yield (t/ha) = (10 x TotFW x DWss / HA x FWss)) (Tarawali et al., 1995).                     (1) 

 

Where: TFW = total fresh weight from the plot in kg DWss = dry weight of the sample in grams 

FWss = fresh weight of the sample in grams. H.A. = Harvest area in meter square, and 10 is a constant for the conversion 

of yields in kg m2 to tone/ha 

 

Analysis of Chemical Compositions 

The methods outlined by AOAC (1990) were used to determine the total ash and crude protein content levels. The methods 

drawn by Van Soest (1988) were used to analyze acid detergent lignin (ADL), neutral detergent fiber (NDF), and acid 

detergent fiber (ADF).  

 

Methods of Data Analyzing  

Before doing the combined analysis, Hartley's test (F-max test) was performed to evaluate the homogeneity of error variance 

(Hartley, 1950). The entire variation was then divided into components remaining to genotype (G), environment (E), and 

genotype with environment (G x E) interaction effects using pooled analysis. The SAS statistical programmed version 

(2002) was used to compute the ANOVA for each location and the total ANOVA over locations. The AMMI and GGE bi-

plots were created using GenStat (2012). 

 

RESULTS AND DISCUSSIONS 

AMMI Analysis of Variance 
The AMMI analysis of variance for forage dry matter yield showed significant (P<0.01) variations in genotype and 

environment but not in effects of the G x E interaction. While genotype and genotype by environment interaction captured 

15.95% and 1.31% of the overall variation, respectively, the environment captured 18.73%. The strong impact of 

environments on the forage dry matter yield performance of lablab genotypes was revealed by a more significant total 

variance caused by environment. Accordingly, different authors have documented significant yield variations of Lablab 

genotypes because of observed environments (Arega et al., 2023). This suggests that the tested Lablab genotypes exhibit a 

significant degree of different reactions to modifications in the growing areas and a differential discriminating capacity in 

the test conditions.  

Additional y AMMI analysis showed that the robust G x E interaction impact could be broken down into principal 

component analysis (PCA). With the first IPCA accounting for 73.83% and the second accounting for an extra 13.1%, the 

first two IPCAs accounted for 86.92%, with IPCA1 being the only important one. Various researchers, Amare and Tamado 

(2014) and Temesgen et al. (2014) suggested that the first two IPCAs may be used to determine the correct model for AMMI. 

The genotypes are more stable or adaptable across all environments sampled when the IPCA scores are closer to zero. 

According to the study authors, there was a substantial difference in dry matter yield for each genotype in the various areas 

(Arega et al., 2023; Oliveira et al., 2014). 

 

Table 1. AMMI ANOVA for forage dry matter yield of 12 lablab genotypes evaluated at 6 locations over two consecutive 

years. 

 

Source of variation D.f SS MS 

% Explained P- values 

Total 

variations 
G X E G X E cumulative  

Total 215 134.16 0.624     

Genotypes 11 38.65 3.513** 15.95   <0.001 

Environments 5 44.84 8.969** 18.73   <0.001 

G x E Interactions 55 15.85 0.288NS 1.31   0.1086 

Blocks (Envts) 12 5.75 0.479** 2.17   0.0163 

IPCA 1 15 6.43 0.428* 1.94 73.83  0.0241 

IPCA 2 13 4.55 0.35NS 1.59 13.1 86.92 0.0956 

Residuals 27 4.88 0.181 0.82   0.7192 

Error 132 29.07 0.220     
D.f= degree of freedom, SS= sum of square, MS= mean sum of square, GXE= Genotype with environment 

 



Jabessa et al., Bangladesh Journal of Multidisciplinary Scientific Research 8(1) (2023), 34-43 

 

37 

The Genotypes' Mean Dry Matter Yield Performances 

The significant relationship between environment and genotypes suggested that various genotypes reacted differently to 

changing environmental conditions. Out of all the investigated genotypes, G-11620 (15.43 t/ha) and G-14486 (11.12 t/ha) 

had the highest forage dry matter yield (Table 2). Compared to the standard checks Beresa-55 and Gabis-17, the dry matter 

yield advantage was 129.9% and 61.2%, respectively. This variation may result from the genotypes' genetic potential. There 

was a significant genotype by environment interaction in terms of dry matter yield, as observed by the variations in yield 

rank of lablab genotypes across tested areas. 

 

Table 2. Lablab genotypes performance for dry matter yields (t/ha) across locations and over the year 

 
Genotypes  Dry matter yield (t/ ha-1) 

2021  2022 

Adola-woyu Kiltu-sorsa Gobicha Adola-woyu Kiltu-sorsa Gobicha 

18622 4.31b 7.96bcd 4.51c 1.64b 11.03a 5.54cd 

Gabis 1.33b 1.07e 1.07d 0.33c 2.27b 1.60d 

10979 4.42b 5.41d 4.52c 1.92ab 10.1a 6.57bc 

10953 4.62b 8.74bcd 4.52c 2.26ab 9.41a 4.86cd 

11620 14.8a 14.63a 17.2a 3.11a 14.0a 16.3a 

Beresa 4.12b 5.65cd 5.61c 1.55b 12.94a 6.77bc 

11630 4.01b 6.93cd 4.6c 1.82ab 9.72a 6.03bcd 

14489 4.2b 8.34bcd 5.11c 1.99ab 10.83a 5.43cd 

14486 12b 11.2b 9.47b 2.42ab 12.67a 10.13b 

11612 4.82b 9.05bc 5.12c 1.72b 10.5a 7.37bc 

14465 5.32b 5.35d 4.92c 1.66b 10.89a 6.18bc 

14474 4.2b 8.3bcd 5.91c 1.98ab 8.48a 5.98bcd 

MEANS 5.68 7.72 6.05 1.86 10.23 6.89 

LSD (5%) 4.05 3.09 2.46 1.16 4.87 4 

CV (%) 4.21 27 2.4 36.8 2.81 3.43 
Means in a column within the same category having different superscripts differ (p<0.05); D.M. t/ha-1 = dry matter yield tone per hectare; LSD=Least Significance difference; 

CV=coefficient of variations. 

 

Composite Agronomic Trait Performances of the Genotypes 
The combined analysis of variance for the genotypes of Lablab purpureus tested across locations for assessed agronomic 

parameters is shown in (Table 3). Except for non-significant differences in plant height and thousand seed weight, the 

genotypes showed significant differences in days to flowering, days to maturity, plant height, number of branches, leaf-to-

steam ratio, number of pods, and number of seeds across the investigated settings. 

Days to 50% flowering were significant (P<0.05) among the genotypes tested. Table 3 shows no variation in the 

plant height at fodder harvest and thousand seed weight between genotypes (P>0.05). This could be because of the 

environment's impact on fodder crops' physiological growth and development. The standard control variety, Gebis-17, took 

longer (111.6 days) to reach physiological maturity than the seed from genotype 11620, which was the shortest (97 days). 

The observed differences could be related to differences in number of days taken to flowering. Early flowering results in 

early physiological maturity for seed harvest. In line with this, KC et al. (2016) reported that lablab genotypes took (81-

130) with 50% flowering, whereas Kankwatsa (2018) reported a shorter number of days and 50% flowering (52 to 69 days).  

The genotypes under-tested had composite mean dry matter yield tones/hectare ranging from 1.44 to 15.43t/ha-1 

in various environments. The reported dry matter yields were 1.44 t/ha, 11.12 t/ha (G-14486), and 15.43 t/ha (G-11620), 

respectively. The most extensive yield advantages over the standard checks, Beresa-55 and Gebisa-17, are shared by two 

genotypes, 11620 and 14486, with yield advantages of 129.9% and 61.2%, respectively. The current study's conclusion is 

consistent with Ogedegbe et al. (2011) earlier report, which stated that the maximum dry matter yield ever recorded was 

10.2 t/ha. Muir (2002) also noted that rainfall significantly influences the dry matter yields of warm-season legumes. 

However, the dry matter yields of the Lablab that were observed in this investigation fell within Mihailovic et al.'s (2016) 

published range of values (1.8-12.9 DM t ha-1). However, lower dry matter yields of 6.8 and 6t ha-1 for various Lablab 

species were recorded (Hidosa et al., 2016). Similarly, Lablab's sub-humid climate in western Oromia recorded a forage dry 

matter yield of 5.4 t ha-1 (Tulu et al., 2018). 

The leaf-to-stem ratio significantly impacts the forage's nutritional quality because leaves have lower fiber content 

and higher nutrient levels than stems. Leaf-to-steam ratios ranged in mean from 0.38 to 0.91. The leaf-to-steam ratio was 

found to be higher in both G-11620 and G-14486. The leaf-to-stem ratio significantly impacts diet choice, forage intake, 

and quality (Zailan et al., 2018). The observed variations among the genotypes examined may be attributed to probable 

genetic variations resulting from environmental interactions. 

 

Table 3. Mean dry matter yields agronomic traits for Lablab genotypes tested in regional variety trials combined at 6 

locations (Adola et al., 2021) over two years, 2021 and 2022. 

 
Genotypes 

 

Days to Plant 

height 

(cm) 

Number 

of 

branches  

Leaf-

to-

steam 

ratio 

Number 

of pods 

per plant 

Number 

of seeds 

per pod 

1000 

seed 

weight 

(g) 

DMY 

t/ha 

DMY yield 

advantage 

over check 

(Barasa-

17) 

DMY yield 

advantage 

over check 

(Gabis-55) 

Flowering 

(days) 

Maturity 

(days) 



Jabessa et al., Bangladesh Journal of Multidisciplinary Scientific Research 8(1) (2023), 34-43 

 

38 

18622 101.56cb 175.2b 96.97 5.3abc 0.573cde 57.4ab 3.79ab 1.66b 6.65c   

Gabis 111.6a 183.4a 100.69 4.de 0.38e      52.9 a 3.8 ab 0.36c 1.44d   

10979 100.7cb 175.7b 100.26 4.7b-e 0.75abc 57.1ab 4.08ab 1.62b 6.45c   

10953 100.5cb 176.6b 95.92 5.3abc 0.66bcd 55.6 ab 3.6a 1.72b 6.86c   

11620 97c 154.3c 100.88 6.48b 0.85ab      87.86b 4.1b 2.26a  15.43a 129.9 971.6 

Barasa 107.7ab 181.6a 99.79 3.8e 0.53de     54.2a 3.79ab 1.62b 6.87c   

11630 102.06cb 177.2b 97.38 4.6b-d 0.68bcd 55.2ab 3.79ab 1.75b 6.43c   

14489 101.8cb 176.9b 100.58 5.3bcd 0.69bcd 56.3ab 3.92ab 1.72b 6.98c   

14486 101.5cb 175.6b 99.63 5.83ab 0.91a    73.4ab 3.97ab 1.82ab 11.12b 61.2 672.2 

11612 101.2cb 176.7b 97.72 4.3cde 0.74a-d 53.6a 3.75ab 1.64b 7.28c   

14465 102.7cb 175.1b 94.37 4.5cde 0.73a-d 51.3a 3.75ab 1.67b 6.55c   

14474 100.8cb 176.3b 100.07 4.7b-e 0.71a-d 56.7ab 3.75ab 2.03ab 6.87c   

Mean 102.4 175.4 98.7 4.9 1.06 59 4 1.6 7.4   

C.v 7.2 2.24 17.3 23.4 4.98 35.8 7.8 4.31 3.84   

LSD  4.8 2.6 11.22 0.76 3.4 19.9 0.3 0.47 1.87   

Means in a column within the same category having different superscripts differ (p<0.05); D.M. t/ha=dry matter yield tone per hectare; LSD=Least Significance difference; 

CV=coefficient of variations. 

 

Ammi Bi-Plot Stability Analysis of Dry Matter Yield 

Thus, the GGE bi-plot has been applied in crop genotype trials to efficiently determine the genotype or genotypes that 

perform best across environments, to determine the best genotypes for the delineation of specific environments so that 

particular genotypes can be recommended to particular environments, and to assess genotype stability and yield (Yan & 

Kang, 2003). Because they were closer to the biplot's centre, G-14486, G-11630, and G-11620 exhibited extensive 

adaptability throughout the settings. The mean genotypes exhibit consistent responses to environmental changes, with a 

small value of IPCA1. Environment: Because the examined materials had longer vectors (possible environments), Kiltu-

Corsa was considered highly discriminating. 

 

  

Figure 1. AMMI 2 bi-plot for IPCA 1 against IPCA 2 scores for 12 genotypes and seven environments 

 

GGE Bi-Plot Analysis  

In the mega-environments (MGE), the genotypes near the polygon's vertices either had the best or worst performance. The 



Jabessa et al., Bangladesh Journal of Multidisciplinary Scientific Research 8(1) (2023), 34-43 

 

39 

polygon view of the GGE bi-plot was the most effective method for identifying winning genotypes and visualizing the 

patterns of interaction between genotypes and environments by Yan et al. (2000) and Yan and Kang (2003). The vertex 

(winning genotypes) in the sector where environments were placed in the MGE sector were genotypes G-11620, G-14486, 

and G-11612. The bi-plot analysis displayed various mega-environments.  

 

Figure 2. The GGE-bi-plot for which -won -where the pattern for genotypes and environments 

Evaluation of Genotypes 

The optimal genotypes for stable and increased dry matter production ability were G-11486 and G-11620, located in the 

centre of concentric circles. Furthermore, the genotypes G-11612 and G-14465, situated on the subsequent concentric circle, 

are also desirable. Compared to other genotypes, genotypes far from the first and second concentric circles such as Gabisa-

17 and others were undesired. Different writers, Arega et al. (2023) on Lablab and Dabessa et al. (2016) on various crops, 

obtained similar results. 

 

Figure 3. GGE-bi-plot based on genotype-focused scaling for comparison of the genotypes 



Jabessa et al., Bangladesh Journal of Multidisciplinary Scientific Research 8(1) (2023), 34-43 

 

40 

Discriminating and Representativeness of Test Environments 

The centre of the concentric circles, or the ideal test environment, can distinguish between genotypes in terms of the 

genotypic main effect and can also better depict the various habitats. The representative testing site and the testing conditions 

(Adola et al., 2021) were quite good at differentiating genotypes. 

 

Figure 4. GGE-bi-plot based on environment-focused scaling for comparison of the environments. 

Mean Performance and Stability of Genotypes 

The most stable genotype in all circumstances is one with a shorter absolute projection length in either of the two AEC 

ordinate directions (positioned closer to AEC). This genotype indicates a lower propensity of the G x E interaction. 

According to their mean performance and stability, high-yielding and stable genotypes were G-14486 and G-18622. 

 

Figure 5. GGE ranking bi-plot shows means performance vs stability 

Chemical Composition  

All chemical compositions among the tested lablab genotypes demonstrated substantial (p < 0.05) changes, according to the 

combined analysis of variances, except DOMD and IVDMD, which did not show significant (p > 0.05) differences (Table 

4). The lowest C.P. content was found in standard cheek Gabis at 10.8%, while the highest and lowest C.P. contents were 

found in G-11620 and G-14486, respectively, at 23.5 and 21.15. Within the 15-30% range, Hector and Jody (2002) reported 

a higher C.P. content in lablab fodder. Murphy and Colucci (1999) found a lower range value of 14.8 to 21.0%. In general, 

the crude protein values observed in this study could satisfactorily supply the crude protein acquirement of the animals' 

ruminants. Therefore, Lablab has a high C.P. value, which can supplement low-quality roughages which could not attain 

the C.P. requirement of ruminant livestock like natural pasture, Rhodes grass, and crop residues with very low C.P., which 

is in line with different authors (Abebe et al., 2015; Asmare et al., 2017).  



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41 

Cell wall components (NDF, ADF, and ADL) showed significant change across genotypes at (P<0.05). Similar 

studies on and other legume species have been reported in the literature. This suggests that the genotypes under test were of 

excellent quality when compared to the bulk of widely used feed supplies in the study area. However, according to Kazemi 

et al. (2012), who identify feeds with NDF (47 to 53%) and ADF (31 to 40%) content as high-quality feeds, all of the 

genotypes under investigation could be regarded as good-quality feed resources and appropriate as a supplement for 

ruminants consuming low-quality feed. The difference observed could be due to soil structure and condition variations and 

genotype differences. 
 

Table 4. Pooled mean chemical compositions of Lablab genotypes tested in regional variety trial from 6 locations (Adola et 

al. 2021) 

Means in a column within the same category having different superscripts differ (p<0.05); D.M. = Dry Matter; C.P. = Crude Protein; OM= Organic matter; NDF =Neutral 

detergent fiber; ADF = Acid Detergent Fiber; Ash= Total ash; ADL= Acid detergent lignin; IVOMD = In vitro Organic Matter Digestibility; IVDMD=In-vitro dry matter 

digestibility CV=Coefficient of variation; LSD=Least Significance difference. 

 

CONCLUSIONS  
The combined analysis of variance showed that the tested Lablab's dry matter yield performances were significantly 

influenced by the environment and genotypes but not by genotype, environments and their interaction (GEI). This suggested 

that different genotypes may react differently to a given environment or that specific genotypes might not function uniformly 

under various environmental conditions. The substantial influence of the environment and genotype interaction on dry 

matter yield raises the possibility of sustained genotype selection with superior dry matter yield performance. The current 

findings suggested that genotype and environment impacted lablab genotype yield and yield components. Consequently, the 

genotypes G-14486 and G-11620, which demonstrated a high dry matter yield and consistent performance, were selected 

for the variety verification trial for additional assessment and potential to release. 
 

 

 

Author Contributions: Conceptualization, T.J.; Methodology, T.J., G.T. and K.B.; Software, T.J.; Validation, G.T., T.J. and K.B.; Formal Analysis, T.J.; 
Investigation, T.J., G.T. and K.B.; Resources, T.J., G.T. and K.B.; Data Curation, T.J.; Writing – Original Draft Preparation, T.J.; Writing – Review & 

Editing, T.J., G.T. and K.B.; Visualization, T.J., G.T. and K.B.; Supervision, T.J., G.T. and K.B.; Project Administration, T.J. and K.B.; Funding 

Acquisition, T.J. Authors have read and agreed to the published version of the manuscript. 
Institutional Review Board Statement: Ethical review and approval were waived for this study because the research does not deal with vulnerable groups 

or sensitive issues. 

Funding: The authors received direct funding from the Oromia Agricultural Research Institute. 
Acknowledgements: Oromia Agricultural Research Institute was acknowledged as a funding source for this activity. Holeta Agricultural Research Center 

is also acknowledged for doing chemical compositions. 

Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. 
Data Availability Statement: The data presented in this study are available on request from the corresponding author. 

Conflicts of Interest: The authors declare no conflict of interest. 

 

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Genotypes D.M. % O.M. % ADL % ADF % C.P. % NDF % IVOMD  IVDMD 

18622 92.1b 82.85a 6.57bc 13.05a-c 15.53cde 15.47ab 42.26 52.66 

Gabis 71.85c 55.1b 12.35ab 21d 10.8e 22.23a 48.36 57.08 

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11630 92.35b 83.75a 7.79bc 14.25a-c 14.51cde 20.9ab 49.02 59.02 

14489 92.9b 82.6a 7.5bc 14.0abc 11.4de 18.8ab 46.87 56.63 

14486 94ab 84.3a 3.6c 9.25a 21.15ab  11.44b 50.40 62.40 

11612 92.05b 83.6a 3.9c 12.88a-c 17.35bc 18.23ab 47.47 55.02 

14465 92.25b 82.55a 7.2bc 15.54a-c 18.23bc 18.23ab 48.06 60.14 

14474 92.65 83.85a 6.39bc 11.6a-c 16.09b-d 16.39ab 47.42 57.17 

Mean 89.74 78.92 6.94 14.14 15.81 16.8 48.17 57.7 

LSD 2.8 5.7 6.12 5.6 4.6 8.47 11.7 15.1 

CV 1.5 3.3 41 18.6 13.4 23 10.6 12.1 



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