- Geleta Negash
- Alemayehu Birr
Journal Name: Agriculture Archives: an International Journal
DOI: https://doi.org/10.51470/AGRI.2026.5.3.08
Keywords: food barley, AMMI, GGEI, performance, stability, yield
Abstract
The field test changed into performed on 14 food barley genotypes along with one local and one currently released well known check (Walashe) at Fitche Agricultural research center at 2023-2024 cropping seasons. The goal was to assess the significance of genotype through environment interaction, grain yield stability, and high yielded and disease resistance genotypes in north shewa zone. The seed changed into sown in randomized completed block design (RCBD) with 3 replications within the plot size of 1.2mx3m. Effective tiller per plant (PPT) Plant height (PH), spike length (SL), days to 50% heading (DH), days to 95% maturity (DM), thousand seed weight (TSW) and grain yield (GY), flag leaf length (FLL), peduncle length (PDL) and awn length had been accrued as agronomic traits. Combined evaluation of variance detected widespread distinction among principal effect of genotypes, year and locations for maximum agronomic developments considered. The interplay impact of genotype via year, location by year, genotype by location by year imposed giant impact particularly on yield however none in all traits. Amongst evaluated genotypes, G-12 with grain yield of 3574.8kgha-1 had significantly better mean grain yield over the rest genotypes and recorded 12.6% yield benefit over standard check (Walashe). The mixed ANOVA and the AMMI analysis for grain yield throughout environments discovered significantly laid low with environments, which maintain 72.4% of the overall variation. The genotype and genotype through environmental interaction were great and accounted for 9.5% and 13% respectively. Principal component 1 and 2 accounted for 6.8% and 3.3% of the GEI respectively with a total of 10.1% variant. In typically, G-12 turned into recognized as promising genotype for grain yield, more stable, tolerant to diseases and can be used as parents for breeding purposes.
1. INTRODUCTION
Barley (Hordium vulgare L.) is identified as one of the international’s most historical food crop, which is thought to have first domesticated about 10,000 years ago from its wild family within the Fertile Crescent of the close to East and center of diversity in Ethiopia [4]. In Ethiopia, Barley is the fifth crucial cereal crop after tef, Maize, Sorghum and Wheat in each general area coverage and annual production [6]. It’s far cultivated at altitudes starting from 1500 to 3500 above sea level and predominantly grown at elevation ranging from 2000 to 3000 m.a.s.l. [21]. Being the maximum dependable and appropriate crop for the highland farmers [9], in a few areas it is cultivated in district seasons: belgs which relies on the quick rainfall duration from March to April and Meher which relies at the long rainfall length from June to September [5]. The maximum vital biotic and abiotic elements that lessen productiveness of barley in Ethiopia include; low yielding types, insect, disease, bad soil fertility, soil acidity and weed competition [5]. Slow increasing of these production constraints are held to be crucial for diminishing productivity of barley.
Multi-location assessment of various barley genotypes is among alternative intervention technique through which productivity of the crop could be alleviated. Subsequently, multi-environment yield trials are crucial in estimating genotype by environment interaction (GEI), figuring out superior and stable genotypes inside the final selection cycles [13; 15]. Enormous GEI results from versions within the quantity of differences amongst genotypes in various environments, called qualitative or rank adjustments, or versions inside the comparative ranking of the genotypes, called quantitative or absolute variations among genotypes [10]. The study of GEI is important in numerous agro-ecologies and enables to remark genotypes adapted to these numerous environments. The additive main effect and multiplicative interaction (AMMI) model also helped us better recognize the complicated patterns of genotypic responses to the environment [11].Therefore, the principal objective of this study was to assess and identify food barley genotypes with high yielder, yield stability and disease tolerant/resistances across check locations and areas with comparable agro-ecologies.
2. Materials and Methods
Description of locations: The test was carried out at different rain fed locations in North shewa zone at Fitche agricultural research center at Degam, Kuyu and Jida sub site all through the 2023-2024 foremost cropping season, that represent distinctive agro-ecologies of the barley growing areas of the zones.
2.1 Experimental materials: Fourteen (14) food barley genotypes, collectively with currently released standard check (Walashe), HB1307 and one farmer’s cultivar, had been included within the trial evaluation. The genotypes were selected based totally on average performance and agro-ecology and were received from Ethiopia biodiversity institute (EBI), Sinana and Holeta agricultural research center.
2.2 Experimental layout and Trial management: Randomized completed block design (RCBD) with 3 replications became used across location. Every experimental plot had six rows of 3m length and 20 cm apart with a plot area of 1.2 m x 3m. Drilling seed with the aid of hand changed into used with the same seed rate for every location. Fertilizer become applied at a rate of 100kgha-1 and 100kgha-1 of UREA and NPS and half of UREA was carried out at some stage in planting time, whilst the rest half of splits have been implemented at tillering stages. Seed rate of 125kgha-1 was used. First weeding was executed 35days after emergence and the second at 30 days after the first weeding conducted. Statistics considered for analysis was from the candidates of the net plot, hence the 4 central harvest-able rows. The harvested genotypes were dried earlier than being tested for moisture content material where 12% changed into the desired average moisture content the usage of moisture tester. Grain yield data was then obtained with the aid of weighing the dried grain using a digital scale.
2.3 Statistics series technique: eight plants have been selected randomly earlier than heading from every row (4 harvest-able rows) tagged and all the vital plant based data had been collected from those sampled plants.
Plot based: the following plant parameters have been determined: Days to heading (DH), Days to maturity (DM), Thousand seed weight (TSW), Grain yield (GY), and the four main economically vital food barley disease which include scald, septoria (SEP), stem rust (SR), and leaf rust (LR)
Plant based: Plant height (PH), productive tillers (PPT), Spike length (SL), Flag leaf length (FLL), Peduncle length (PDL), and Awn length (Awl)
2.4 Statistical evaluation
Analysis of variance is calculated using the model:
Yij = µ + Gi + Ej + GEij
Where in: Yij is the corresponding variable of the i-th genotype in j-th environment, μ is the total mean, Gi is the main effect of i-th genotype, Ej is the main effect of j-th environment, GEij is the effect of genotype x environment interplay. Yij = µ + gi + ej + ∑_1^N ʎk Ƴik δjk + Ɛij
2.5 AMMI model
in which: Yij is the grain yield of the i-th genotype in the j-th environment, µ is the grand mean, gi and ej are the genotype and environment deviation from the grand mean, respectively, ʎk is the principal component analysis (PCA) axis k, Ƴik and δjk are the genotype and environment principal component score for axis k, N is the number of principal components retained in the model, and Ɛij is the residual term.
2.6 AMMI Stability Value (ASV)
ASV is the gap from the coordinate factor to the beginning in a two-dimensional plot of IPCA1 rankings in opposition to IPCA2 rankings inside the AMMI model [18]. Because the IPCA1 score contributes more to the GxE interaction sum of squares, a weighted value is needed. This weighted value calculated for each genotype and every environment in step with the relative contribution of IPCA1 to IPCA2 to the interaction sum of squares as follows:
ASV=√ [(SSIPCA1 / SSIPCA2) (IPCA1score)] 2 + (IPCA2score) 2
Where: SSIPCA1/SSIPCA2 is the weight given to the IPCA1-value by dividing the IPCA1 sum of squares with the aid of the IPCA2 sum of squares. The larger the ASV value, either negative or positive, the more especially adapted a genotype is to a certain environments. Smaller ASV values imply more stable genotypes across environments [18].
2.7 Genotype selection Index (GSI)
Stability isn’t only the best parameter for choice as maximum stable genotypes might not necessarily give the best yield performance. Therefore, based at the rank of mean grain yield of genotypes (RYi) throughout environments and rank of AMMI stability value RASVi), genotype selection index (GSI) become calculated for each genotype as:
A genotype with the least GSI is considered as the most stable [8].evaluation of variance became completed using R software program (model 4.2.2).
3. RESULTS AND DISCUSSIONS
3.1 Combined analysis of variance
The mean square analysis of variance (ANOVA) is provided in table 2. Significant differences were detected a number of the essential and the interaction effects (P ≤ 0.01) for most of the parameters evaluated. The mixed evaluation of variance found out that significant differences have been recorded across location for all parameters. Year*genotypes and year*location*genotypes have been excreted considerable effect in particular on grain yield however none significant for most traits.
.
3.2 Yield overall performance throughout locations: mean performance for grain yield throughout location and year offered in table three. Study performed [14] indicated that, some genotypes continuously carried out excellent in a few environments and fluctuating across a few locations. for this reason, the average grain yield ranged from the lowest of 1037.1kgha-1 at kuyu sub site in 2023 to the best of 3735.8kgha-1 on similar site in 2024 with grand mean of 2875.8kgtha-1.The average grain yield across environments ranged from the lowest of 2373kgha-1 for G-3 to the highest of 3574.8kgha-1 for G-12 which constantly changed into done and gave better yield over the examined environments
3.3 Agronomic performance: combined mean grain yield and other agronomic traits are organized in table4. From the evaluated genotypes, Genotype (G-12) became recorded medium days to heading, days to maturity, and plant height, which become an option to broaden resistant variety towards accommodations problems. It additionally recorded the highest thousand seed weight and grain yield. In the same way, G-4 was recorded medium days to heading, days to maturity, effective tillers and thousand seed weight. Those suggested that, there is remarkable flexibility for developing advanced types that suitable genotypes for various agro-ecologies with variable duration of growing length and high in grain yield repute. Furthermore, these genotypes (G-12 and G-4) have been recorded the best grain yield and that they had 12.6%, and 6.5% yield advantage over the standard check (walashe) respectively.
3.4 Predominant disease reactions over location: maximum genotypes evaluated had considerably low reactions with their corresponding economically important disease. But, genotypes (G-9 and G-10) had been moderately susceptible to septorial, leaf and stem rust disease. On the other hand, genotype (G-12) was better tolerance to septorial, stem and leaf rust than the rest genotypes
3.5 AMMI analysis for grain yield: The additive main effects and multiplicative interaction analysis of grain yield showed that, environment and genotypes via environment interaction had been tremendously significant (P≤0.01). This is similar to the report [17].This suggests that one of the simple elements that affect GEI could either be genotypic or environmental in nature [7; 2] also report that 74.3% of the interaction sum of squares was defined by way of IPCA1.
The environment and genotype mean are supplied in Fig.1 and table 7. This bi-plot helped within the interpretation of the interplay outcomes among genotypes and environments; and in the assessment of the adaptability of genotypes. Genotypes which can be characterized by using mean, more than grand mean and the IPCA scores almost zero are taken into consideration as normally adaptable to all environment. But, the genotypes with high mean overall performance and with large value of IPCA scores are taken into consideration as having specific adaptability to the environments [19]. Study carried out [3] stated that, the larger the IPCA scores, either negative or positive, the more particularly adapted a genotype is to a certain environments; yet the smaller the IPCA scores, the greater stable the genotype is over all environments. Primarily based on those, genotype (G-8) had grain yield above the grand mean; and similar IPCA1 scores with location Degam and kuyu implying that their interactions were effective; the higher yield of this genotype become observed, in particular, at these location. Therefore, it is the best adapted genotypes for those locations.
3.6 Stability evaluation for genotypic performance: The AMMI stability values (ASV) for grain yield is offered in table7. Considering the AMMI stability value that takes into consideration, the scores of the IPCA2, genotypes with least ASV scores are the most stable, at the same time as genotypes with high ASV score are unstable [3; 8]. Consequently, genotype (G-12) became regarded to be amongst the ones showing low ASV and became the more stable. At the opposite this, genotype (G-6) revealed the highest ASV and become hence taken into consideration to be unstable. Stability by way of itself be supposed to, however, no longer be the best parameter for choice, as the most stable genotype could no longer essentially provide the first-class yield performance [16]. So, the study indicated that, G-1 was recorded the lowest ASV, however recorded decrease grain yield of 2654kgha-1 than the grand means of 2875.8 kgha-1. The stable genotype became accompanied with mean grain yield above the grand mean and this result was in agreement with [12], who has used ASV as one method of comparing yield stability of bread wheat types and comparable reports been made with the aid of [1] in barley and bread wheat using AMMI stability value. A genotype with the least of genotype selection index (GSI) is considered because the maximum stable genotype [8]. Primarily based on that reason, G-12 became more stable genotype with the low of genotype selection index (GSI) and better yield.
Conclusions and recommendations
Grain yield partitioned in to one-of-a-kind IPCAs the use of AMMI model evaluation, that the first principal component axis for interaction alone explains most of the interplay sum of squares. The sign and magnitude of IPCA scores showed the relative contribution of every genotype and environment for the genotype by environment interactions. It facilitates to summarize the pattern and significance of GEI and fundamental outcomes that screen clean insight into the adaptation of genotypes to environments. This suggests that, genotype (G-12) is less contributors to the interaction effect and has consistent performances throughout locations. Consequently, it was identified as promising genotype in phrases of yield capacity and stability, tolerant to disease and endorsed for development, launch and use as parent’s materials in purpose of breeding programs.
Acknowledgements
The authors greatly mentioned Oromia Agricultural research Institute (IQQO) for financial support. Fitche Agricultural research center staff members are warmly acknowledged for technical and administrative assist. Particularly, all cereal research case team members significantly appreciably thankful for his or her technical aid and subject work and as properly Ethiopia biodiversity institute (EBI) Sinana and Holeta agricultural research center are acknowledged for the provision of test materials.
REFERENCES
[1] Abay F. and Bjørnstad A., Specific adaptation of barley varieties in different locations in Ethiopia. Euphotic, 167: 181-195 (2009)
[2] Anandan A., Eswaran R., Sabesan T. and Prakash M., Additive main effects and multiplicative interactions analysis of yield performances in rice genotypes under coastal saline environments, Indian Journal of Advances Biological Research 3(1-2), 43-47 (2009).
[3] Bantayehu M.,Analysis and correlation of stability parameters in malting barley. African Crop Science Journal 17 (3), 145-153 (2009)
[4] Bedassa Mokonin. Selection of Barley varieties for their yield potential at low rainfall area based on both quantitative and qualitative characters North West Tigria, Shire, Ethiopia. International Journal of plant breeding and genetics. ISSN -1819-35. (2014).
[5] Bekele Hundie. Infection, biology, epidemiology and resistance of barley land race lines to pyrenophora teres. A PhD Thesis submitted to graduate School of Kasetsart University, Thailand. (2005).
[6] Central statistical Authority (CSAAgricultural sample survey. Report on area and production of crops (Private peasant holding, meher season). Statistical Bulletin No. 578. Addis Ababa, Ethiopia. ). (2015).
[7] Debelo D., Girma B., Alemayehu Z. and Gelalcha S., Drought tolerance of some bread wheat genotypes in Ethiopia. African Crop Science Journal 9 (2), 385-392 (2000)
[8] Farshadfar E.,Incorporation of AMMI stability value and grain yield in a single nonparametric index (GSI) in bread wheat. Pakistan Journal of Biological Sciences 11:1791-1796 (2008).
[9] Ferdissa Eticha, Woldeyesus Sinebo and Heinrich Grausgruber. On-farm Diversity and Characterization of Barley (Hordeum vulgare L.) Landraces in the Highlands of West Shewa, Ethiopia. Ethnobotany Research & Applications 8:025-034. (2010).
[10] Fernandez GCJ. Analysis of genotype x environment interaction by stability estimates. Hort Science.; 26:947–950. (1991).
[11] Gauch HG., Statistical analysis of yield trials by AMMI and GGE. Crop Sciences. 46:1488-1500 (2006).
[12] Hintsa G. and Abay F., Evaluation of Bread Wheat Genotypes for their Adaptability in wheat growing Areas of Tigray Region, northern Ethiopia. Journal of Biodiversity and Endangered Species, (2013)
[13] Kaya, Y., Akcura, M. andTaner, S. GGE-bi-plot analysis of multi- environment yield trials in bread wheat Turkish Journal of Agriculture 30: 325-337. (2006).
[14] Mengistu, G., Lule, D., Desalegn, K., Daba, C., Feyisa, H., Gerema, G., & Mamo, K. Registration of “Chemeda and Gemedi” Sorghum Varieties East African Journal of Sciences, 7(1), 61-62 (2013).
[15] Mitrovic, B., Stanisavljevi, D., Treski, S., Stojakovic, M., Ivanovic, M., Bekavac, G. and Rajkovic, M. Evaluation of experimental Maize hybrids tested in Multi-location trials using AMMI and GGE bi-plot analysis. Turkish Journal of Field Crops 17: 35-40. (2012).
[16] Mohammadi R., Armion M., Shabani A. and Daryaei A.,Identification of stability and adaptability in advanced durum wheat genotypes using AMMI analysis. Asian Journal of Plant Sciences 6:1261-1268 (2007).
[17] Ntawuruhunga P.H., Rubaihayo P., Whyte J.B.A., Dixon A.G.O. and Osiru D.S.O., Additive main effects and multiplication interaction analysis for storage root yield of cassava genotypes evaluated in Uganda. Africa Crop Sciences Journal 9 (4), 591-598 (2001)
[18] Purchase JL., Parametric analysis to describe genotype x environment interaction and yield stability in winter wheat. Ph.D. Thesis, Department of Agronomy, Faculty of Agriculture of the University of the Free State, Bloemfontein, South Africa (1997)
[19] Singh D., Singh S.K. and Singh K.N., AMMI analysis for salt tolerance in bread wheat genotypes.www.cabdirect.org/abstracts/ 20093259042.html.Accessed on 10 February 2011 (2009)
[20] Sivapalan S., Obrien L., Ortiz-Ferrara G., Hollamby GJ., Barclay I. and Martin PJ., An adaptation analysis of Australian and CIMMYT/ICARDA wheat germplasm in Australian production environments. Crop Science pastures 51: 903-915 (2000).
[21] Tamene Mideksa. Registration of “Robera” newly released food barley (Hordeum vulgare L.) variety for highland altitude areas of Ethiopia. Scientific Journal of Crop Science 5(5): 116-120. (2016).