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Research Article | Open Access

Volume 2019 |Article ID 1398612 | https://doi.org/10.1155/2019/1398612

Yirga Kindie, Aemiro Bezabih, Wubeshet Beshir, Zinabu Nigusie, Zelalem Asemamaw, Antenh Adem, Birke Tebabele, Genet Kebede, Tesfay Alemayehu, Fentaw Assres, "Field Pea (Pisum sativum L.) Variety Development for Moisture Deficit Areas of Eastern Amhara, Ethiopia", Advances in Agriculture, vol. 2019, Article ID 1398612, 6 pages, 2019. https://doi.org/10.1155/2019/1398612

Field Pea (Pisum sativum L.) Variety Development for Moisture Deficit Areas of Eastern Amhara, Ethiopia

Academic Editor: Ayman Suleiman
Received13 Oct 2018
Revised10 Feb 2019
Accepted06 Mar 2019
Published24 Mar 2019

Abstract

Twelve field pea genotypes were evaluated in seven environments in Eastern Amhara in main production season (2010-2012). The objective of this trial was to identify stable and high yielding field pea genotype for production in Eastern Amhara. The trial was conducted using randomized complete block design with three replications. Combined analysis of variance for grain yield revealed that genotypes, environments, and genotype by environment interaction effect were highly significant (P ≤ 0.01). The environments, GEI, and genotypes were accounted for 77.47%, 13.83%, and 4.37%, of the total sum squares, respectively, indicating that field pea grain yield was significantly affected by the changes in the environment, followed by GEI and genotypic effect. The candidate genotype, EH-03-002, showed 14.42% and 44.87% yield advantage over the standard and local checks, respectively. Considering the seven environments data and field performance evaluation during the variety verification trial, the National Variety Releasing Committee has approved the official release of EH-03-002 with the vernacular name of “Yewaginesh” for moisture deficit areas of Wag Lasta and similar agroecologies.

1. Introduction

Field pea (Pisum sativum L.) is one self-pollinated diploid (2n=14) annual of the most important annual cool season pulse crop and is valued as high protein food [1]. It is widely grown in the cooler temperate zones and in the highlands of tropical regions of the world. The crop is cultivated in a wide range of soil types from light sandy loams to heavy clays but it does not tolerate to saline and waterlogged soil conditions [2]. The soil pH optimum is 5.5-6.5. Field pea is one of the most important pulse crops in Ethiopia which is produced for a long time in high- and mid-altitude areas by smallholder farmers. It covers an area of about 25147.69 hectares with an annual production volume of 21406364 kg [3]. Field pea is nutritious food staff when fully matures and they are valuable food legume, often being ground into flour and used extensively in the manufacture of soups. Fresh green peas are almost universally accepted as a nutritious vegetable [4].

Nutritionally, field peas contain all the essential amino acids and are rich in high-quality vegetable protein [5]. Therefore, this crop can substitute high protein containing animal meat products in the developing countries including Ethiopia. The crop has an important role in the highlands of Ethiopia by playing a significant role in soil fertility improvement occupying a unique position in cereal-based cropping systems [6]; the crop is considered environmentally friendly and economical feasible from soil improvement point of view. Despite its importance, the average national productivity (0.85 tha−1) is very low [3]. It is below the potential as compared to the research findings that ranged from 0.82 tha−1 to 4.6 tha−1 in Ethiopia [7] and the higher yield reported about 7 tha−1 to 8 tha−1 in Europe (England and France) [8]. The major yield-limiting constraints in field pea production in Ethiopia are aphids, low yielding local varieties, lodging, diseases (ascochyta blight, powdery mildew), and pod shattering [9] (Yayis et al., 2014). Ethiopian field pea landraces showed lack of resistance gene for two major field pea insect pests, namely, pea aphid and pea weevil (Melaku et al., 2003). Kemal (2002) identified options for the control of field pea pests such as the use of mixed cropping, time of planting and fertilizer application, cultural control, and biological control. On the other hand, several studies revealed that there is valuable genetic variability in field pea collections for yield and yield-related traits [9, 10] which is a good opportunity for Ethiopian field pea researchers to exploit the genetic potential of the crop by integrating conventional with molecular techniques. In general, we believe that the current field pea research should be assisted by more efficient molecular tools to develop varieties that are resistant or tolerant to field pea insect pest.

In Ethiopia, in order to increase field pea productivity, more than 30 improved varieties have been developed and released by national and regional Agricultural Research Center of the country. Most of them are targeted to favourable and wide environments but they are not suited for diverse and challenging environments. In general genotype by environment (GxE) interaction affects the efficiency of crop improvement programs that may lead to complicates recommendation of varieties across divers’ environments. Therefore, information on the structure and nature of GxE interaction is particularly useful to breeders (Bridges, 1989; Yayis et al., 2014). However, there is limited information on the extent of GEI in Eastern Amhara in general and in Waghimra in particular. Hence, this trial was initiated with the following objectives:(1)To determine and understand the effect of genotype, environment, and their interaction on grain yield of field pea.(2)To identify and release stable and high yielding field pea genotype for Wag Lasta and similar agroecologies of the country.

2. Materials and Methods

2.1. Genotypes, Testing Sites, and Experimental Design

Twelve field pea genotypes advanced from observation nursery and preliminary variety trials, together with local check and standard check (Agrit), were evaluated during the main cropping season for four convictive years (2010 to 2012) in two sites and each site and year were treated as a single environment. The trial was conducted using a randomized complete block design with 3 replications throughout the testing sites. Description of the testing sites is presented in Table 1. The trial was conducted on the plot size of 4m0.8m with four rows per plot throughout all trial sites and 1.5m between replication, 1m between plot, 20cm between rows, and 5cm between plant distances were maintained. A variety verification trial was conducted at both Lalibela and Aybera on the trial station and in seven farmers’ field during 2016/17 main production season.


LocationYearsEnvironment CodeAltitudeAnnual rain fallSoil Type
(m.a.s.l)

Aybera2010, 2011, 2012 & 2013E1, E2, E3 & E41850789vertisol and red sandy soil
Lalibela2010, 2011 & 2012E5, E6 & E72400940Black (vertisol) soil

2.2. Data Collection

Data on grain yield and yield-related traits were collected on plot and plant basis from each plot, respectively. Date of flowering and maturity were taken when each plot attained 50% flowering and 90% of the pod’s physiological maturity, respectively, and days were calculated beginning from the date of sowing. Data for plant height (cm), number of pods per plant, and number of seeds per pod were collected on the basis of five sample plants which were randomly taken from each plot and the average of five sample plants was used for analysis. Hundred seed weight (g) and grain yield (g) of each plot were measured on clean, dried seed and the measured grain yield value (g) has converted to kilogram per hectare for analysis. All agronomic practices were done as per the recommendation for field pea.

2.3. Data Analysis and Analysis of Variances

Data from individual environments and combined over seven locations were analyzed by using SAS (2009) software. The analysis of variance for grain yield and yield-related traits for each environment and over seven environments was analyzed by using randomized complete block design. The combined analysis of variance across the environment was done in order to determine the differences between genotypes across environments, among environments and their interaction. Bartlett’s test was used to assess the homogeneity of error variances prior to doing combine analysis over environments. Mean comparison using Duncan's Multiple Range Test (DMRT) was performed to explain the significant differences among means of genotypes and environments.

The combined analysis of variance across environments was done in order to determine differences between field pea genotypes across the environment, among the environment, and also to determine their interaction effect by using the following statistical model:where is observed mean of the genotype (Gi) at the environments (Ej), μ is the general mean, , , and represent the effects of the genotype, environment, and genotype by environment interaction, respectively, R (E) is the effect of replications within environments, and is the average random error associated with the plot that receives the genotype in the environment.

ANOVA from Additive Main Effect and Multiplicative Interaction (AMMI) [11] was computed for grain yield by using Gen Stat software ( edition) that exhibited significant mean squares for genotype and genotype by environment interaction.

3. Results and Discussion

3.1. Analysis of Variance and Mean Performance of 12 Field Pea Genotypes

Analysis of variance for each environment showed a highly significant (p ≤ 0.01) variation for grain yield and yield-related traits among the tested field pea genotypes (Table 2). This indicated the presence of performance variation among the tested field pea genotypes for grain yield and yield-related traits across the testing environments. Likewise, Mulusew et al. [12] and Tamene et al. [7] in field pea and Mulusew et al. [13] in faba bean had reported significant variation for grain yield and most of the yield-related traits among the tested genotypes across testing environments.


GenotypesGen. CodeEnvironment CodeGen. mean
E1E2E3E4E5E6E7

IFPI12712G119411081227328822115145913511872
EH-03-001G2148397625653125199914758251778
COLL-24/002-1G3226387727033347196695013651924
EH-03-002G420451183252733612446208224002292
EH-03-010G51408867259734842757148712201974
COLL-217/99-5G61971741232926962633126012631842
EH-99-009-8G71317621252731202307144318851889
IPF-4021G81894869226724482409117822301899
EH-03-018G91751791216128301967177816501847
EH-99-020-3G101995823244626292545145512431877
Agrit (ch)G1114871140258030172218137722022003
Local (ch)G12203762319232855213538511201582

Env. Mean1799883240829832291136115631898
CV%11.68.88.06.06.412.511.79.7
Duncan 5%

= significant at 1% probability level, = significant at 5% probability level, CV = coefficient of variation (%), Env. mean= environmental mean, Gen. Code = genotype code, and Gen. mean = genotypic mean.

As indicated in Table 2, the average environmental grain yield across genotypes varied from the lowest 621 kg ha−1 at E2 (Aybera in 2011) to the highest at 3484 kg ha−1 in E4 (Aybera in 2012). The highest yielding environment (E4) had 57.16% yield advantage over the environmental gran mean. Environments, E3 and E5, were the second and the third highest yielding environments with mean grain yield of 2408 and 2291 kg ha−1, respectively.

The mean grain yield of field pea genotypes across seven environments ranged from 1582 kg ha−1 for the local check to 2292 kg ha−1 for G4 (EH-03-002) (Table 2). Moreover, performances of genotypes were not consistent across seven environments. For instance, at E2 (Aybera 2011) genotype EH-03-002, at E3 (Aybera in 2012) genotype COLL-24/002-1, at E4 (Aybera in 2013) genotype EH-03-010, and at E6 (Lalibela 2011) genotype EH-03-002 were the top ranking genotypes with the mean grain yield of 1183 kg ha−1, 2703 kg ha−1, 3484 kg ha−1, and 2082 kg ha−1, respectively. Thus, such inconsistent yield ranking from the environment to environment indicated the presence of possible cross over GEI as described by Ermiyas [14] and Matova and Gasura [15].

3.2. Combined Analysis of Variance and Mean Performance of Genotypes over Seven Environments

The combined analysis of variance for grain yield and yield-related traits of twelve field pea genotypes tested in seven environments had performed on the original (untransformed) data (Table 3). The analysis showed that field pea grain yield was significantly (p ≤ 0.01) affected by environment, genotype, and genotype by environment interaction. The significance of GEI indicated that the relative performances of the genotypes were not consistent across the test environments and the environments had different effects on the yield potential of the genotypes. This, in turn, suggested the need to conduct further analysis on genotype by environment interaction to understand the nature of the interaction. These results agreed with previous findings of Tamene et al. [7] which reported that genotypes, environments, and GxE interaction were significantly different for grain yield of field pea. Likewise, other authors also documented a significant GxE interaction for grain yield in field pea [12] and in faba bean [13]. Similarly, analysis of variance indicated a significant effect of genotypes for all collected yield-related traits and significant environmental effect for days to flowering, number of pods per plant, number of seeds per pod, and hundred seeds weight while tested genotypes showed significant variation in days to flowering, number of seeds per pod, and hundred seed weight due to the G X E interaction effect.


GenotypesDFDMPPSPPHHSWGY

IFPI12712509375127161872
EH-03-001539585131171778
COLL-24/002-1539376111131924
EH-03-002539376135182292
EH-03-010539476120161974
COLL-217/99-5549585126161842
EH-99-009-8559885126181889
IPF-4021539675133181899
EH-03-018539586131171847
EH-99-020-3539475124151877
Agrit (ch)509285129212003
Local (ch)539576130121582

Mean539575127161898
Cv %4.222.6623.9813.4811.447.289.66
Genotype
EnvironmentNsNs
GXE interactionNsNsNs

Ns, , and = nonsignificant, significant at p < 0.05, and significant at p < 0.01, respectively, DF = days to flowering, DM = days to maturity, PP = pods/plant, SP = seeds/pod, HSW = 100 seed weight (g), GY = grain yield (kg/ha), and CV = coefficient of variation (%).

Among the tested genotypes, EH-03-002 was the highest yielder genotype with the mean grain yield of 2292 kg/ha followed by standard check Agrit (2003 kg ha−1) and genotype, EH-03-010 (1974 kg ha−1), respectively, whereas the lowest mean grain yield (1582 kg ha-1) was registered from a local check (Table 2). As indicated in the same Table 3, five genotypes scored highest grain yield over the grand mean (i.e., 1898 kg ha-1) but only one candidate genotype scored mean grain yield above the standard check Agrit. The candidate genotype, G4 (EH-03-002), was statistically high yielder (2292 kg ha−1) than the other genotypes and showed 14.42% and 44.87% yield advantage over the standard check: Agrit (2003 kg ha−1) and local check (1582 kg ha−1), respectively. This genotype has been verified in 2017 and visited by the national variety releasing technical committee. Accordingly, genotype EH-03-002 has been officially released for its high yielding, deep white grain colour, and high adaptability in the moisture deficit areas of Wag Lasta.

3.3. Additive Main Effects and Multiplicative Interaction Analysis for Grain Yield

The additive main effects and multiplicative interaction (AMMI) analysis for grain yield showed highly significant (p ≤ 0.01) effect of environment, genotype, and genotype by environment interaction. The effects of environment, genotype, and genotype by environment interaction accounted for 77.47%, 13.83%, and 4.38% of the total sum of squares, respectively (Table 4). A large sum of squares for environments indicated that the environment was diverse, with large differences among environmental means causing most of the variation in grain yield. This also designated the reliability of the multienvironment experiments. The variation in soil type, soil fertility, and moisture availability might be the main reasons for the presence of variation among environment. In line to this Ermiyas [14] has reported the highest contribution (51.6%) of environmental effect for total variance of soybean grain yield. Similarly, Massaine et al. [16] in cowpea and Tadesse et al. [17] in common bean noticed the highest variation explained by the environmental effect.


SourceDfSSMS Sum of square Explained
% Total%G X E% Cumulative Interaction

Total251140958296561587
Genotypes1161697705608884.38
Environments61091986681819977877.47
Block14914635653310.65
Interactions661949349829535613.83
IPCA11673801104612575.2437.8637.86
IPCA21454517383894103.8727.9765.83
IPCA31228762392396872.0414.7580.58

Residuals243785411157725
Error154518172633648

Grand Mean (kg/ha) =1897.60CV(%) = 13.49R-Square = 0.93

= highly significant at the 0.01 probability level, = significant at the 0.05 probability level, Df = degree of freedom, and IPCA: principal component axis for interaction.

AMMI analysis (Table 4) also showed that the first interaction principal component axis (IPCA1), the second interaction principal component axis (IPCA2), and the third interaction principal component axis (IPCA3) of the interaction explained 37.86%, 27.97%, and 14.75% of the interaction sum of squares, respectively. The mean squares for the IPCA1 and IPCA2 were Significant at P ≤ 0.01 and cumulatively contributed to 65.83% of the total GxE interaction. The third interaction principal component axis (IPCA 3) was also significant (p ≤ 0.05). For the validation of the variation explained by GEI, the first two multiplicative component axes are adequate because of notable reduction of dimensionality and graphical visualization for the stability patterns of genotypes [18]. According to Yan et al. [19] and Annicchiarico (2002), the AMMI model can be predicted by using the first two IPCAs. Numerous authors utilized the first two IPCAs for AMMI analysis in different crops: for faba bean [13], for field pea (Tamene et al., 2008), for finger millet [20], and for triticale [21]. Thus, the interaction of the 12 field pea genotypes with seven environments was best predicted by the first two principal components of genotypes and environments.

4. Conclusion and Recommendation

Genotype by environment interaction has a key effect on crop variety development by complicating the release of varieties across challenging environments. Analysis of variance for every seven locations and combined over seven locations showed significant differences among genotypes, environments, and genotypes x environments interaction (GEI) for grain yield and most of the yield-related traits. The significant genotypes x environments interaction effects indicated the inconsistent performance of genotypes across the tested environments. Among the tested genotypes, EH-03-002, Agrit, EH-03-010, COLL-24/002-1, and IPF-4021 had mean grain yield above the overall mean grain yield of genotypes. Only the candidate genotype EH-03-002 had mean grain yield above the standard check Agrit.

Additive main effects and multiplicative interactions (AMMI) model was used to partition the G x E interaction of grain yield of field pea. The results indicated that the environments, GEI, and genotypes were accounted for 77.47%, 13.83%, and 4.37%, of the total sum squares, respectively, indicating that field pea grain yield was significantly affected by the changes in the environment, followed by GEI and genotypic effect.

Considering the seven environments data and field performance evaluation during the variety verification trial, the national variety releasing committee has approved the official release of candidate genotype, EH-03-002, with the vernacular name of “Yewaginesh” for moisture deficit areas of Wag Lasta and similar agroecologies.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

The authors would like to thank staff members of the Crop Technology Supply Research Directorate of Sekota Dry Land Agricultural Research Center for their unreserved effort in trial management and data collection. The site keepers, Mr. Kefeyalew Girmaye and Mr. Mekonent Tadu, are also acknowledged for their unreserved effort in trial management and strong following up. Finally, we wish to acknowledge the Amhara Regional State Government for financial support and the directors at ARARI and Sekota Dry Land Agricultural Research Center for helping and facilitating us to conduct this study and come to completion with fruitful result.

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Copyright © 2019 Yirga Kindie et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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