Genotype x environment interaction studies for earliness and grain yield in cowpea (Vigna unguiculata (L.) Walp.)

Introduction

Cowpea is an annual herbaceous and self-pollinated grain legume crop that belongs to family Fabaceae and genus Vigna. It is a diploid species with 2n = 2x = 22 chromosomes (Ogbemudia et al. 2010) [26] . The precise primary center of origin and the exact center of diversity of cowpea is not known, but according to OECD, Ethiopia is the primary center of diversity of cowpea and more than 66.5% of the arable land is very suitable for cowpea production. Cowpea is drought resistant and adapted to marginal soil due to the nitrogen-fixing ability that makes it a useful staple crop for farmers in harsh environments under moisture stress and hot temperatures (Hill, 2015; Noubissietchiagm et al. 2010) [18,23] . It is widely cultivated and consumed globally, especially in the arid and semiarid tropics and sub-Saharan Africa (Baidoo and Mochiah 2014) [5] . The crop is cultivated primarily for its edible seeds, young leaves (as a vegetable), pods seeds, soil improvement, and forage value (Mulugeta et al. 2017 and Alemu, 2015) [21,3] . Despite the fact that cowpea is a multipurpose grain legume, its productivity is very low and unstable across environments and years due to biotic factors (weeds, insects, and diseases), abiotic factors (soil type, altitude, rainfall distribution, and intensity), and scarcity of widely adaptable and early-maturing varieties (Fonji, 2015, Horn et al. 2015) [13,19] . The variability in environments, such as location effects, seasonal fluctuations, and their interactions, highly influences the production of cowpea in relation to yield potential (Adewale et al. 2011; Hall et al. 2003) [2,15] . Genotype × environment interaction (GEI) is a challenge for plant breeders since it complicates variety recommendation because of the inconsistency of best-yielding genotypes across cropping environments and seasons. The effect of GEI is most commonly minimized by repeating the experiment at several sites in a single year, over several crop seasons in a single site, or both (Gauch et al. 1996 and Bandeira et al. 2018) [14,6] . Even though there are many opportunities for breeders to develop cowpea varieties possessing different agronomic characteristics and inherent tolerance to a wide range of biotic and abiotic factors, progress in cowpea breeding is very limited, either in exploiting the available genetic variability in the country or through the introduction of improved varieties. Moreover, information on the effect of genotype, environment, and their interaction and the performance stability of cowpea landraces is scanty. Therefore, evaluating cowpea genotypes in contrasting environments to generate such information is imperative. In view of this, this research was initiated with the following objectives: (i) To estimate the effects of genotype, environment, and genotype × environment interaction on earliness and grain yield (ii) To assess the stability of cowpea genotypes for earliness and grain across environments.

Materials and Methods

Description of the Study Area

The experiment was conducted at three cowpea-growing environments during the Kharif 2023 main cropping season in Marathwada region of Maharashtra, India. The environments were Parbhani, Badnapur and Aurangabad. These three locations representing different agroecologies growing areas in Maharashtra and descriptions of the areas are presented in table 1.

Experimental Materials and Design:

Female lines were the released cultivars and the donor lines were collected from NBPGR, New Delhi. The features of parental lines used in present study are presented in (Table 2). Sixty F 1 crosses were generated from crossing five lines and twelve testers in a line x tester mating system during Kharif-2022. Seventeen parents including lines and testers, sixty F 1 crosses along with three check (PL 3, DC 15 and RC 10) were evaluated. The experiment was conducted using randomized block design with two replication each at three different locations and each genotype was assigned randomly. The seeds were planted with two rows, with an interrow spacing of 45 cm and 10 cm between seeds within the row. The length of row was 4.00 m 2 . All agronomic management practices were carried out equally and properly as per the recommendations.

Data Collection and Data Analysis.

Data for yield and yield related traits were collected on the basis of five sample plants which were randomly taken from the two central rows, and the average of five samples was used for analysis, The collected data were analyzed using R software, employing the linear regression model of stability proposed by Eberhart and Russell (1966) [11] .

Results and Discussion

Analysis of Variance for earliness and grain yield

An analysis of variance showed that the mean squares owing to environments (E) and genotypes (G) were determined to be extremely significant. (Table 3) for three characters under study. The findings showed that the genotypes and settings under study were diverse. Similar results are in agreement with those Nunes et al. (2014) [24] , El. shaieny et al. (2015) [12] and, Sharma et al. (2022) [31] in cowpea who reported large variation performance among different tested cowpea genotypes across environments. Mean square due to E + (G × E) component was significant for days to 50% flowering and variations due to environment (linear) were significant for days to 50% flowering, days to maturity and grain yield/plant revealing the linear contribution of environmental effects and additive environment variance (Patel and Jain 2012., Chaudhari et al. 2013., and Nassir et al. 2021) [28,8,22] . The majority of the overall variance could be explained by the environments' linear response, as seen by the larger magnitude of mean squares attributed to environments (linear) than genotypes × environments (linear). The non-linear components of G × E interaction (pooled deviation) were significant for days to 50% flowering, days to maturity and grain yield/plant. This implied that the differential response of stability comprised both predictable and unexpected components for this trait. (Ddmuliral et al. 2015 [10,4,20] .

Stability analysis earliness and grain yield:

According to Eberhart and Russell's model, a stable genotype should have high mean yield, the regression coefficient approximating to a unity (bi = 1), and deviation from regression (S 2 di) nearly equal to zero. The regression coefficient (bi) measures the response of genotypes to environments. When the regression coefficient of the genotype is nonsignificantly different from unity (bi = 1), the genotype is said to have average response to environmental change and suitable to both poor and good environments, while if the bi value of genotypes is significantly greater than unity (bi > 1), the genotype is highly responsive to environmental change and it is suitable only for a good environment. When the genotype bi value is significantly less than unity (bi < 1), it indicates that the genotype is less responsive and it is suitable for poor environments. The stability parameters among all the experimental genotypes are presented in Table 4. In present study, the unit regression coefficient and non-significant value of departure from regression were shown for average stable parents and crosses. Because of their lower mean (preferred) and non-significant value of regression deviation, unit regression coefficient (bi) values are thought to be generally stable and adaptable to a variety of events in days to flowering parents (PL 4 and IC626138) and crosses were Phule Sonali x SNAGR 12-33 (Mean 38.00 days, bi = 1.09, S 2 di = -0.52), Phule Vithai x EC 101998 (Mean = 45.50 days, bi = 0.90, S 2 di = 0.42), and Phule Vithai x IC 326997 (Mean 46.17 days, bi = 1.01, S 2 di = -0.66), depicted stable performance and suitability to varied environments. The crosses showed earliness in 50% flowering with wider adaptability and it may be utilised for the development of early flowering improved varieties. The cross PL 4 × EC 738277 (Mean = 40.17 days, bi = 1.21, S 2 di = -0.15) exhibited non-significant deviation from regression, mean value less than population mean (47.13 days), with regression coefficient more than unity appeared suitable for favourable environment for days to 50% flowering. These results confirmed the findings of Adewale et al. (2010) [1] . Pal et al. (2020) [27] who found that the genotype Paras was the most stable for days to 50% flowering across all environments. Sharma et al. (2022) [31] found that the genotype GC 1602 exhibited desirable stability parameters for days to 50% flowering in cowpea. For days to maturity, low mean value than population mean (73.32) and non-significant values of regression coefficient (bi < 1) and deviation from regression (S 2 di <0) were desirable. Among all the seventeen parents, none of line and tester exhibited stable performance across the three environments for days to maturity. However, the lines PL 4 (Mean = 57.33 days, bi = -0.52, S 2 di = 0.79) and Phule Vithai (Mean = 68 days, bi = -0.46, S 2 di = -0.75) exhibited low mean, coefficient less unity and least nonsignificant deviation from regression depicted suitability for low yielding environments. While, the cross Phule Sonali x IC 329764 (Mean = 64.33 days, bi = 0.99, S 2 di = -0.78) expressed low mean coupled with bi = 1 and least non-significant deviation from regression showed its stability across the all types of environment. However, the cross PL 4 × EC 724805 (Mean = 65.50 days, bi = 1.13, S 2 di = -0.09) followed by GC 3 × EC 724805 (Mean = 66.17 days, bi = 1.19, S 2 di = -0.72) and Phule Sonali × IC 68786 (Mean = 69.83 days, bi = 1.20, S 2 di = -0.70) expressed low mean coupled with coefficient more than unity and least non-significant deviation from regression depicted below average stability to favourable environments. Further, the cross combination GC 3 × IC 202931 (Mean = 72.83 days, bi = 0.86, S 2 di = -0.26) had regression coefficient less than unity, and recommended to unfavourable environments only. These results were in consonance with the findings of Singh (2003) [33] who stability of 15 genotypes of cowpea lines regarding days to maturity. Similar results were also reported by Chauhan et al. (2004) [9] , Adewale et al. (2010) [1] , Patel and Jain (2012) [28] , Simion et al. (2018) [32] and Sharma et al. (2022) [31] for days to maturity. In case of grain yield, the stability analysis evinced that crosses like Phule Vithai x IC 622602 (Mean 22.67 g, bi = 1.08, S 2 di = -0.47), GC 3 x IC 329764 (Mean 19.83 g, bi = 0.97, S 2 di = -0.72), Phule Sonali x EC 101958 (Mean 25.53 g, bi = 0.91, S 2 di = -0.09), Phule Sonali x IC 329764 101958 (Mean 27.87 g, bi = 0.93, S 2 di = -0.72) exhibited high mean value than papulation mean (16.69 g), regression coefficient near unity and minimum deviation from regression line manifested stable performance to all three environments. Such crosses showing stable performance across the environments may be useful in a breeding programme for evolving high yielding cowpea crosses well adapted to varying environments. Crosses showing stable performance across different environments may be advantageous in a breeding endeavour geared toward developing early, high-yielding cowpea crosses that are well-suited to various conditions. Furthermore, the crosses viz., Phule Rakhumai × SNAGR 12 [29] and Adewale et al. (2011) [2] reported differences in cowpea genotypes for their responsiveness and stability for grain yield tested at different environments. Chattopadhyay et al. (2001) [7] also reported EC 2543954, Sel-Tm1, and Sel-Tm3 emerged as relatively stable genotypes for yield in cowpea. Whereas, Henry et al. (2003) [17] noted that genotype V-585 exhibited consistent performance across varying environmental conditions, while genotype GC-3 was identified as well-suited for adverse environments. The harmony in results were reported by Patel and Jain (2012) [28] who revealed that for improvement of grain yield, the genotype GC-0121 was most stable and found 20% superior over the popular check variety GC-5. On another hand, El-shaieny et al. (2015) [12] reported stability parameters for genotypes Chinese Red, IT81D1064, IT85F2205 and Sudany were identified as stable for total dry grain yield. Likewise, Havaraddi and Deshpande (2018) [16] noted genotypes DC 15, DCS 47-1-1(F), C 152×IC 202872 (F) and C 152 IC 202860 (F) were identified as stable and well-adapted across environments for grain yield. In the present investigation, upon examining these factors for every genotype independently, it was found that none of the parents or hybrids demonstrated average stability across all features. Because the genotype for these traits may not display uniform responsiveness and stability patterns at the same time, it is impossible to generalize about the stability of the genotype for all of the characters. It is consequently advised that in order to develop stable hybrids, genuine hybrids would need to be tested in a variety of conditions, and the choice of parents should be made primarily on how well they combine. It has been found that the stability of its component features leads to the genotype stability for green pod yield per plant. Nunes et al. (2014) [24] , Santos et al. (2015) [30] , Manivannan et al. (2019) [20] and Sharma et al. (2022) [31] suggested incorporating stability into breeding operations by using prospective and stable genotypes.

References

1. Adewale BD, Okonji C, Oyekanmi AA, Akintobi DAC, Aremu CO. Variability and stability of some grain yield components of cowpea. African Journal of Agricultural Research. 2010;5(9):874-880.
2. Adewale BD, Adeigbe O, Aremu C. Genetic distance and diversity among some cowpea (Vigna unguiculata L. Walp) genotypes. International Journal of Research in Pharmacy and Science. 2011;1(2):9-14.
3. Alemu S. Cowpea (Vigna unguiculata (L.) Walp.) (Fabaceae) landrace diversity in Southern Ethiopia [MSc Thesis]. Addis Ababa: Addis Ababa University; 2015.
4. Aquino DAL, Antonio DA, Santos FA, Silva DOM. Adaptability and stability parameters for immature seeds and pods and mature dried seeds in cowpea genotypes in Brazil Northeast. African Journal of Agricultural Research. 2016;11(50):5071-5079.
5. Baidoo PK, Mochiah MB. Varietal susceptibility of improved cowpea (Vigna unguiculata (L.) Walp.) cultivars to field and storage pests. Sustainable Agriculture Research. 2014;3(2). https://doi.org/10.5539/sar.v3n2p69
6. Bandeira M, Jackson K, de Moura M, Angelo J, Raphaelle L. Genotype by environment interaction in cowpea lines using GGE biplot method. Universidade Federal Rural do Semi-Árido. 2018;31(1):64-71.
7. Chattopadhyay A, Dasgupta T, Hazra P, Som MG. Phenotypic stability for green pod yield and its components in cowpea (Vigna unguiculata (L.) Walp.). Indian Agriculturist. 2001;45(3-4):141-146.
8. Chaudhari SB, Naik MR, Patil SS, Patel JD. Stability analysis in cowpea (Vigna unguiculata (L.) Walp.). Trends in Biosciences. 2013;6(4):450-556.
9. Chauhan RM, Solanki SD, Shah DS, Patel JB, Tikka SBS. Stability analysis in cowpea (Vigna unguiculata (L.) Walp.). Gujarat Agricultural University Research Journal. 2004;29(1-2):19-21.
10. Ddmuliral G, Santos CAF, Obuo P, Alanyo M, Lwangal CK. Grain yield and protein content of Brazilian cowpea genotype under diverse Ugandan environments. American Journal of Plant Sciences. 2015;6:2074-2084.
11. Eberhart S, Russel WA. Stability parameters for comparing varieties. Crop Science. 1966;6:36-40.
12. El-Shaieny AAH, Abdel-Ati YY, El-Damarany AM, Rashwan AM. Stability of components characters in cowpea (Vigna unguiculata (L.) Walp.). Journal of Horticulture and Forestry. 2015;7(2):24-35.
13. Fonji AKM. Genetic analysis of traits related to biological nitrogen fixation in cowpea (Vigna unguiculata L. Walp.) under low soil phosphorus [PhD Thesis]. Accra: University of Ghana; 2015.
14. Gauch HG, Zobel RW, Kang MS. AMMI analysis of yield trials. In: Genotypes by Environment Interaction. New York: CRC; 1996. https://doi.org/10.1201/9781420049374.ch4
15. Hall AE, Cisse N, Thiaw S, Elawad H, Ehlers JD, Ismail AM, et al. Development of cowpea cultivars and germplasm by the bean/cowpea CRSP. Field Crops Research. 2003;82(2-3):103-134. https://doi.org/10.1016/S03784290(03)00033-9
16. Havaraddi U, Deshpande SK. Genotype-environment interactions and stability analysis in advanced promising lines of cowpea (Vigna unguiculata (L.) Walp.). Plant Archives. 2018;18(1):401-409.
17. Henry A, Kumar D, Singh NB. Genotype x environment interactions for seed yield in cowpea. Advances in Arid Legume Research. 2003;387-389.
18. Horn L, Shimelis H, Laing M. Participatory appraisal of production constraints, preferred traits and farming system of cowpea in northern Namibia: implications for breeding. Legume Research. 2015;38(5):691-700. https://doi.org/10.18805/lr.v38i5.5952
19. Manivannan N, Kumar KB, Mahalingam A, Ramakrishnan P. Stability analysis for seed yield in cowpea genotypes (Vigna unguiculata (L.) Walp.). Electronic Journal of Plant Breeding. 2019;10(3):1246-1249.
20. Mulugeta A, Asefaw Z, Woldu Z, Amsalu B, Medvecky B. Cowpea (Vigna unguiculata (L.) Walp.) (Fabaceae) landrace diversity in northern Ethiopia. International Journal of Biodiversity and Conservation. 2017;8(11):297309.
21. Nassir AL, Olayiwola MO, Olagunju SO, Adewusi KM, Jinadu SS. Genotype × environment analysis of cowpea grain production in the forest and derived savannah cultivation ecologies. Agro-Science. 2021;20(2):20-24.
22. Noubissietchiagm JB, Bell JM, Guissaibirwe S, Gonne S, Youmbi E. Varietal response of cowpea (Vigna unguiculata L. Walp.) to Striga gesnerioides (Wild.) Vatke race SG5 infestation. Horticulture Agrobotanici Cluj-Napoca. 2010;38(2):33-41.
23. Nunes HF, Francisc RF, Valdenir QR, Regina LF. Grain yield adaptability and stability of black-eyed cowpea genotypes under rainfed agriculture in Brazil. African Journal of Agricultural Research. 2014;2:200-61.
24. OECD. Consensus document on the biology of cowpea (Vigna unguiculata (L.) Walp.). ENV/JM/MONO. 2015;48(60).
25. Ogbemudia FO, Denise EM, Ogie-Odia EA, Omonhinmin AC. Comparative germination studies of cowpea (Vigna unguiculata L. Walp.) and soybean (Glycine max L. Merr.) on whole and water saturated fractions of hydrocarbon (hexane). Annals of Biological Research. 2010;1(4):34-40.
26. Millspaugh) genotypes for yield and its component traits. Int J Curr Microbiol App Sci. 2020;9(9):264-271.
27. Patel PR, Jain SK. Stability analysis for yield and yield component traits in new breeding lines of cowpea (Vigna unguiculata L. Walp). Legume Research. 2012;35:23-27.
28. Rodriguez M, Rau D, Papa R, Attene G. Genotype by environment interactions in barley (Hordeum vulgare L.): different responses of landraces, recombinant inbred lines, and varieties to Mediterranean environments. Euphytica. 2007;163(2):231-247.
29. Santos C, Ceccon G, Rodrigues EV, Teodoro PE, Makimo PA, Alves VB, et al. Adaptability and stability of cowpea genotypes to Brazilian Midwest. Afr J Agric Res. 2015;10:3901-3908.
30. Sharma M, Patel MP, Patel PJ, Patel PR. Stability analysis of yield and yield attributing traits in advanced breeding lines of cowpea [Vigna unguiculata (L.) Walp.]. Electronic Journal of Plant Breeding. 2022;13(3):901-909.
31. Simion T, Mohammed W, Amsalu B. Genotype by environment interaction and stability analysis of cowpea [Vigna unguiculata (L.) Walp] genotypes for yield in Ethiopia. Journal of Plant Breeding and Crop Science. 2018;10:249-257.
32. Singh RC. Stability of cowpea lines under weather conditions of semiarid region. Advances in Arid Legume Research. 2003;383-386.