alexa
Reach Us +44-1477412632
Genotype and#215; Environment Interaction and Stability Estimate for Grain Yield of Upland Rice Genotypes in Nigeria | OMICS International
ISSN: 2375-4338
Rice Research: Open Access
Make the best use of Scientific Research and information from our 700+ peer reviewed, Open Access Journals that operates with the help of 50,000+ Editorial Board Members and esteemed reviewers and 1000+ Scientific associations in Medical, Clinical, Pharmaceutical, Engineering, Technology and Management Fields.
Meet Inspiring Speakers and Experts at our 3000+ Global Conferenceseries Events with over 600+ Conferences, 1200+ Symposiums and 1200+ Workshops on Medical, Pharma, Engineering, Science, Technology and Business

Genotype × Environment Interaction and Stability Estimate for Grain Yield of Upland Rice Genotypes in Nigeria

Maji AT1, Bashir M2, Odoba A1, Gbanguba AU1 and Audu SD1*

1National Cereals Research Institute Badeggi, Niger state. Nigeria

2National Biotechnology Development Agency, Lugbe, Abuja, Nigeria

Corresponding Author:
Audu SD
National Cereals Research Institute Badeggi
Niger state, Nigeria
E-mail: [email protected]

Received December 23, 2014; Accepted February 26, 2015; Published February 28, 2015

Citation: Maji AT, Bashir M, Odoba A, Gbanguba AU, Audu SD (2015) Genotype × Environment Interaction and Stability Estimate for Grain Yield of Upland Rice Genotypes in Nigeria. J Rice Res 3:136. doi: 10.4172/2375-4338.1000136

Copyright: © 2015 Maji AT, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Visit for more related articles at Rice Research: Open Access

Abstract

Genotype × environmental interaction and stability estimate were investigated on grain yield of 30 upland rice varieties at Sabon Daga, Amakama, Yandev and Uyo in 2003. The experiments were laid out in a randomised complete block design with three replications. AMMI Anova for grain yield revealed no significant different among genotypes (P<0.01), but there is significant difference on environments and the interaction. The significant different on the interaction indicates that, the genotypes respond differently across the different environments. The partitioning of GGE through GGE biplot analysis showed that principal component1 and principal component 2 accounted for 62.21% and 28.57% of GGE sum of squares respectively, explaining a total of 90.78% variation. AMMI 2 biplot revealed that, genotype ART16-9-3-15-3-B-1-1 (8) gave the highest mean yield of 2925 kg/ha with high main additive effect better than the check varieties. Hence, the genotype would be considered more adapted to wide environments than the rest of genotypes. Environments, such as Sabon Daga and Amakama could be regarded as a more stable site for high yielding rice varieties compare to the other locations.

Keywords

Genotype; Stability; ANOVA

Introduction

Rice (Oryza sativa L.) is the second most important cereals crop, grown in more than 144 million farm worldwide, most certainly than any other crop on a harvested area of about 162 million ha [1]. The author also reported that, global rice production rise more than tripled between 1961 and 2010, with a compound growth rate of 2.24% per year, most of the increase in rice production was due to higher yields, which increase at annual average rate of 1.74%, compared with an annual average growth rate of 0.49% for area harvested. He further stated that, per capital consumption of rice continues to grow fast particularly in most sub-Saharan Africa, where high population growth with changing consumer preferences is causing rapid expansion in rice consumption. In countries such as Kenya, Niger, Nigeria and Tanzania people are moving away from maize and cassava to rice as their income rises. Along with strong population growth, the rapid rise in per capita consumption also contributed to such rapid growth in rice demand.

In Nigeria, rice is a leading staple crop cultivated in virtually all the agro-ecological zones of country, from the mangrove and swamps environment of the coastal areas, to the dry zones of the Sahel in the North [2]. On the other side, the demand for rice has been soaring over years, since mid 1970’s rice consumption in Nigeria has risen tremendously growing by 10.3% per annum, as a result of accelerating population growth rate, increasing per capita consumption, rapid urbanization, increase income levels, and associated changes in family occupational structures [2-4]. GRISP [1] reported that, Nigeria is blessed with three major rice production environments and their coverage is rainfed lowland (69.0%), irrigated lowland (2.7%) and rainfed upland (28.3%). More than 90% of Nigeria’s rice is produced by resource poor small-scale farmers, while the remaining 10% is produced by cooperate/commercial farmers.

Upland rice is grown in rainfed, naturally well drained soils without surface water accumulation, normally without pyretic water supply, and normally not bunded. In the upland environment, rice cultivation is challenged by drought, low adoption of improved varieties, soil acidity and general soil infertility, poor weed control, limited capital investments, labor shortages and low mechanization, resulting in low yield range from 1.0 to 1.7 t/ha compared with a potential of 2.0-4.0 t/ha. Most upland rice is grown on small subsistence farms with few purchased inputs and most production is for family consumption. Therefore developing high yielding upland varieties combine with tolerant to biotic and abiotic stress will contribute substantially to poverty alleviation, especially, for resource constrained households and can increase household food security.

Numerous statistical methods have been developed for the analysis of Genotype by Environment Interactions (GEI) and phenotypic stability [5-8]. Regression technique has been widely used [9,10] due to its ease and the fact that its information on adaptive response is easily applicable to locations. The Principal Component Analysis (PCA) method that shows the mean squares of the principal components axes [11] has also been used. [12] Zobelet al. compared the traditional statistical analysis such as Analysis of Variance (ANOVA), Principal Component Analysis (PCA) and Linear Regression with AMMI analyses, and showed that the traditional analyses were not always effective in analyzing the multi-environment trial data structure. The ANOVA is an additive model that describes main effects effectively and determines if GE interaction is a significant source of variation, but it does not provide insight into the patterns of genotypes or environments that give rise to the interaction. The PCA is a multiplicative model that contains no sources of variation for additive G or E main effects and does not analyze the interactions effectively. The linear regression method uses environmental means, which are frequently a poor estimate of environments, such that the fitted lines in most cases account for a small fraction of the total GE and could be misleading [13-15].

Additive main effects and multiplicative interaction (AMMI) has been proved to be a suitable method for depicting adaptive responses [15-17]. AMMI analysis has been reported to have significantly improved the probability of successful selection [17] and has been used to analyse GxE interaction with greater precision in many crops [13,15,18]. The model combines the conventional analysis of variance for genotype and environment main effects with principal components analysis to decompose the GEI into several Interaction Principal Component Axes (IPCA). With the biplot facility from AMMI analysis, both genotypes and environments are plotted together on the same scatter plot and inferences about their interaction can be made.

This study, reports the use of AMMI model to analyse yield data of thirty genotypes of upland rice evaluate in four locations. The objectives is (1) to determine the nature and magnitude of G × E interaction effect on grain yield in diverse environment (2) to determine environment where upland rice genotypes would be adapted and produce economically competitive yield.

Materials and Methods

Thirty upland rice varieties selected from breeding task force upland mega environmental trial (MET) of 2012 are composed as preliminary yield trial (PET) in National Cereals Research Institute, Badeggi rice breeding unit, evaluated during 2013 cropping season at four locations as shown in Table 1. The experiment was conducted in a randomised complete block design in three replication, The plot size was 4 m × 3 m square with 20 cm inter and intra row spacing. Fertilizer application was 40 kg N, 40 Kg P2O5 and 40 Kg K2O at transplanting, while additional 40 kg N per ha was used as top dressing at vegetative and panicle initiation in equal split. Weed control was by chemical at 21 days after transplanting (DAT) using a formulation of Propanil and 2-4-D (Orizo Plus(R)), and was followed by hand weeding at 43 days after transplanting. Grain yield was recorded after harvest at 14% moisture content and was subjected to analysis of variance (ANOVA) using Crop Stat statistical package. In order to determine the effect of genotype × environment interaction on rice grain yield, the data was further subjected to an additive main effect and multiplicative interaction (AMMI), GGE-biplot and Boxplot analysis using Breeding View (BV) statistical package.

Location Longitude Latitude State Agro-Ecological Zones
SabonDaga 090.73’N 060.52’ E Niger Southern Guinea Savannah
Amakama 050.29’ N 070. 33’ E Abia Rain Forest zone
Uyo 040. 50’ N 070. 56’ E AkwaIbom Rain Forest zone
Yandev 080.47’ N 070.22’ E Benue Southern Guinea Savannah

Table 1: Geographic description of coordinates of the trial location in 2013 cropping season.

Results and Discussion

AMMI analysis of variance

The fit of an additive model to the rice grain yield data are presented in Table 2. It showed that, there is no significant difference in genotypes main effect. However, significant differences (P<0.01) exist among environments and genotypes × environment (G × E) interaction, PCA1 and PCA2 main effects. The environments are characterised by the average performance of the genotypes at a particular environment and the results indicates that, the environments differ significantly. Marcos et al. [19] reported that, environmental difference is not a major concern, but the differences that exist between the genotypes. No significant genotype main effect indicates that genotypes are not different in their mean performance across environment. Although genotypic and environmental scores are deemed to represent genetic and environmental qualities, they come from a mathematical procedure, a principal components analysis on the GEI [12,20] that maximizes the variation explained by the products of the genotypic and environmental scores. The first two PCA explains most of the variation,in grain yield. This is reflected in Table 2, which shows the results from the AMMI model to the grain yield data. In the AMMI model, GEI is explained by two axes (principal component 1, PCA1, and principal component 2, PCA2) that are highly significant respectively, both with an associated (P<0.001). Thus the interaction of the 30 genotypes across four environments was best predictable by the first two principal components.

Source d.f. s.s. m.s. v.r. F pr
Genotypes  29  3120724  107611  1.20  0.2529
Environments  3  79273389  26424463  295.33 <0.001
Interactions  87  7784210  89474  3.37 <0.001
 IPCA 1  31  4842595  156213  5.88 <0.001
 IPCA 2  29  2224187  76696  2.89  0.0035
 Residuals  27  717428  26571    

Table 2: AMMI analysis of variance for grain yield of 30 rice genotype across 4 environments.

Box Plot is a convenient way of graphically depicting group of numerical data through their qualities. It displays varieties in samples of a statistical population without making any assumptions of the underlying statistical distribution [21]. The spacing between the different parts of the box indicates the degree of dispersion (spread) of the data and allows visually estimate of inter-quartile mean, median and mode. Result in Figure 1 is showing the distribution pattern of grain yield of 30 rice genotypes across four environments. The result revealed that, Sabon daga has the highest mean grain yield of 3692 kg/ha (Table 3) with large variance followed by Amakama with mean yield of 2940 kg/ha, while Yandev and Uyo discriminate less between genotypes with mean of 1719 and 1846 kg/ha, respectively. This is reflected in the smaller variance Przystalski [22] reported that, the genetic variance tends to be larger in better environments than in poorer environments.

rice-research-boxplot-showing

Figure 1: Boxplot showing the distribution pattern of grain yield among 30 rice genotypes across four environments.

Location Range Lower quartile Upper quartile Std. d Mean %cv s. sq
S Daga 1477 3405 4004 398.4  3692 10.79 4603301
Amakama 1600 2733 3167 406.3  2940 13.82 4787551
Uyo 358 1768 1911 98.7  1846 5.35 282616
Yandev 839 1606 1861 206.1  1719 11.99 1231466

Table 3: Showing the statistical distribution of environmental performance

A desirable property of the AMMI model is that, the genotypic and environmental scores can be used to construct powerful graphical representations called biplots [19] that help to interpret the GEI, the biplot showing both genotypes and environments in the same plot. The author further stated that, biplots facilitate the exploration of relationships between genotypes and/or environments. Genotypes that are more similar to each other are closer to each other in the plot than genotypes that are less similar. The same is true for environments. Genotypes/environments that are alike tend to cluster together. Result in Figure 2 indicates that, S Daga location has the highest mean yield 3692 kg/ha, while ART12-1L6P7-8-1-B-1 (2) is the genotype with the highest mean yield. The result also shows that, there is no correlation between Amakama and Yandev/ Uyo locations. The projection of ART12-1L6P7-8-1-B-1 (2) and ART16-9-3-15-3-B-1-1 (8) on to S Daga axis reflects the higher mean yield performance of the genotypes. Similarly in Amakama genotype ART3-9L9P3-1-B-2 (22) and ART2- 6L6P6-1-B-1(10) performed best in the location, while genotype ART12-1L6P7-8-1-B-1 (2) and ART16-9-3-15-3-B-1-1 (8) has positive interaction with S Daga. It is also predicted that, genotype ART3- 3L12P9-1-1-B (15), ART3-7L9P8-3-B-B-2(20) and ART3-6L3P9- B-B-2 (16) has negative GEI values in S Daga because their projections were towards the negative direction of S Daga arrow. Also genotype FARO55 (23), ART16-22-1-1-2-B-1-1 (7) and WAB706-27-K5-KB-2 (28) have negative interaction with Amakama location. Generally, there was a poor yield performance in Yandev and Uyo locations as shown in Figure 2.

AMMI 2 biplot display

In the AMMI 2 biplot, (Figure 2) the environmental scores (locations) are joined to the origin by side lines. Sites with short vectors do not exert strong interactive forces (Uyo and Yandev). While those that long vectors exert strong interaction (S. Daga and Amakama). Weikai Yan reported that, a short vector indicates a location in which there is a small range of genotype performance.

rice-research-Biplot-AMMI

Figure 2: Biplot of AMMI for 30 rice genotypes across four environments.

The vertical Y axis is showing the best one dimension measure of the GE effect for each genotype. Thus, genotypes close to the X axis have a small GE effect, while those far away the X axis in either the positive or negative directions has a large GE effect. Figure 2 shows that, genotype ART10-1L12P2-1-B-1(1) and ART16-16-5-23-1-B-1-1 (6) has a small GE effect, which is considered stable and less influenced by the environments.

Weikai Yan reported that, If the angle between two genotype vectors is less than 90 degrees, then the genotypes are positively correlated, tending to do well, or badly, in the same environment. But if the angle between the vectors of two genotypes is greater than 90 degrees, then they tend to perform differently over the trial environments. If the angle between two genotype vectors is 90 degrees, their performance is independent, of each other. Figure 2 shows that, ART16-9-3-15- 3-B-1-1 (8), ART3-9L9P3-1-B-2 (22) and ART3-6L3P9-B-B-3 (17) are positively correlated. However, there is negative correlation between ART16-9-3-15-3-B-1-1 (8), ART10-1L12P2-1-B-1 (1) and ART16-16- 5-23-1-B-1-1(6). Also, there is no correlation between ART16-9-3-15- 3-B-1-1 (8) and ART3-12L11P2-B-B-1 (11) ART16-12-22-4-1-B-1-1 (5), ART3-8L6P6-5-B-2(21) and FARO58 (24). The ideal genotype is the genotype with high performance combined with good stability.

GGE biplot also allows the partitioning of environment into groups. In this study, three environmental groups are identified as shown in the Figure 3. S Daga and Amakama in the upper part are two different environments, while Yandev and Uyo close to each other at the origin form one similar environment. The partitioning of GGE through GGE biplot analysis of grain yield showed that, PC1 and PC2 accounted for 44.63% and 42.32% of GGE sum of squares respectively, explaining a total of 86.95% variation. GGE biplot shows the cosine of the angle between two environment vectors is proportional to the correlation between those two environments that is an angle of less than 90 degrees. The environments are positively correlated [21]. The result in Figure 3 shows a negative correlation between S Daga and Amakama indicating that different genotypes performed differently across the two environments. The distance between S Daga and Amakama in the GGE biplot is related to the independence of the genotype performance in the two environments, while the closeness of Yandev and Uyo location signifies that genotypes response patterns are similar in yield performance. Therefore to save resources, it is better to select only one location out of this group for further trials, Yandev location could be selected in group 3 as it has the longest vector (Tables 4 and 5).

rice-research-GGE-biplot

Figure 3: GGE biplot for best rice genotypes in different environments for grain yield.

S/no. Genotype Location Mean Grain Yield (Kg/ha) MeanGrain yield (Kg/ha)
SabonDaga Amakama Uyo Yandev  
1 ART10-1L12P2-1-B-1 3150 3333 1870 1611 2491
2 ART12-1L6P7-8-1-B-1 4627 2500 1743 1739 2652
3 ART15-4-14-63-2-B-1 3214 2367 1810 1678 2267
4 ART16-12-17-3-4-B-1-1 4004 2800 1849 1936 2647
5 ART16-12-22-4-1-B-1-1 4248 2767 1861 2028 2726
6 ART16-16-5-23-1-B-1-1 3225 3267 1810 1681 2496
7 ART16-22-1-1-2-B-1-1 3417 2167 1794 1369 2187
8 ART16-9-3-15-3-B-1-1 4429 3500 2016 1756 2925
9 ART16-9-4-17-3-B-1 3856 2967 1771 1708 2576
10   ART2-6L6P6-1-B-1 3405 3767 1854 1939 2741
11 ART3-12L11P2-B-B-1 3428 3067 1712 1611 2455
12 ART3-12L2P1-B-B-1 4255 2933 1804 1778 2693
13   ART3-1L6P5-1-B-1 3783 3300 1714 1450 2562
14   ART3-2L4P5-1-B-1 3599 3167 1910 1300 2494
15 ART3-3L12P9-1-1-B 3334 3100 1918 2083 2609
16   ART3-6L3P9-B-B-2 3291 2700 1767 1817 2394
17   ART3-6L3P9-B-B-3 3954 3500 2028 1494 2744
18   ART3-7L3P3-B-B-2 4132 3167 1843 2139 2820
19 ART3-7L9P8-1-B-B-2 3468 3067 1671 1861 2517
20 ART3-7L9P8-3-B-B-2 3166 2800 1931 1917 2454
21   ART3-8L6P6-5-B-2 4048 2767 1790 1894 2625
22   ART3-9L9P3-1-B-2 3423 3700 1992 1583 2675
23 FARO55(NERICA1) 3712 2200 1740 1833 2371
24 FARO58(NERICA7) 3576 2800 1986 1689 2513
25 FARO59(NERICA8) 3354 2667 1722 1756 2375
26 NERICA11 3680 3100 1768 1597 2536
27                   NERICA18 3446 2733 1911 1394 2371
28 WAB706-27-K5-KB-2 3679 2367 1903 1606 2389
29 WAB788-16-1-1-2-HB 4121 2833 1993 1606 2638
30          WAB891-SG12 3732 2800 1887 1728 2537
  LSD @ 0.05% NS NS NS NS NS
  % CV 19.7 24.3 7.4 10.2 14.3

Table 4: Mean grain yield of upland rice varieties across four location in Nigeria.

 s/no. Trt/no. Genotype Sensitivity Mean Mean square deviation
1 3 ART15-4-14-63-2-B-1 0.6666 2453 7633
2 13 ART3-1L6P5-1-B-1 0.7323 2267 22924
3 18 ART3-7L3P3-B-B-2 0.7335 2609 49011
4 28 WAB706-27-K5-KB-2 0.7814 2394 5782
5 22 ART3-9L9P3-1-B-2 0.8351 2374 5527
6 5 ART16-12-22-4-1-B-1-1 0.8481 2491 201906
7 7 ART16-22-1-1-2-B-1-1 0.8652 2496 141668
8 9 ART16-9-4-17-3-B-1 0.8931 2187 114581
9 29 WAB788-16-1-1-2-HB 0.8947 2371 199216
10 16 ART3-6L3P9-B-B-2 0.9020 2513 10073
11 10 ART2-6L6P6-1-B-1 0.9223 2517 52360
12 30 WAB891-SG12 0.9226 2741 337543
13 23 FARO55(NERICA1) 0.9326 2388 107737
14 4 ART16-12-17-3-4-B-1-1 0.9480 2371 38952
15 15 ART3-3L12P9-1-1-B 0.9724 2454 43037
16 25 FARO59(NERICA8) 0.9836 2537 8713
17 11 ART3-12L11P2-B-B-1 0.9957 2675 324508
18 1 ART10-1L12P2-1-B-1 1.0467 2647 55744
19 14 ART3-2L4P5-1-B-1 1.0767 2536 17852
20 26 NERICA11 1.0896 2625 68563

Table 5: Twenty environmentally sensitive rice varieties in grain yield across four upland rice growing environments in 2013 using AMMI analysis.

Conclusion

AMMI statistical model is a tool in selecting the most suitable and stable high yielding crop genotype for specific as well as for diverse environments. In the present study, AMMI model has shown that the largest proportion of the total variation in rice grain yield in the genotypes is attributed to environments. Most of the genotypes showed environment specificity. The mean grain yield value of genotypes averaged over environments indicated that ART16-9-3-15-3-B-1-1 (8) had the highest mean grain yield 2925 kg/ha. Genotypes ART10- 1L12P2-1-B-1(1) and ART16-16-5-23-1-B-1-1 (6) has a small GE effect, which is considered as stable and less influenced by the environment.

References

Select your language of interest to view the total content in your interested language
Post your comment

Share This Article

Relevant Topics

Article Usage

  • Total views: 12324
  • [From(publication date):
    April-2015 - Feb 18, 2019]
  • Breakdown by view type
  • HTML page views : 8493
  • PDF downloads : 3831
Top