Printed in the Philippines Adaptability and stability analysis of newly-released rice varieties...

Printed in the Philippines

ASIA LIFE SCIENCES 23(2): 516-527, 2014The Asian International Journal of Life Sciences

Received 01 October 2013; Accepted 18 January 2014©Rushing Water Publishers Ltd. 2014

Adaptability and stability analysis of newly-released rice varieties using GGE biplot analysis

A.D. PALAnOg1*, C.A. EnDinO1, i.M.g. SiOCOn1, L.T. STA. inES1 and E.M. LibETARiO1

Varietal adaptability can both be wide or specific adaptation which generally depends on the mean grain yield and yield stability over wide range of environments. Variety trial or multi-environment trial is an important tool in selecting for the appropriate rice variety/varieties specifically or widely adaptable to an environment or range of environment which could eventually improve the productivity of farmers. The study aimed to evaluate the mean yield performance and yield stability of newly-released rice varieties, examine the relationship of the varieties, and characterize the different test locations. Variety trial was conducted in the six provinces of Western Visayas, Philippines using six newly-released rice varieties. Result of combined analysis of variance (AnOVA) revealed a strong gE interaction on the yield performance of the different varieties established on various environments. Moreover, environment and genotype independently exerted significant main effects. GGE biplot analysis graphically displayed the gE interaction, relationship of varieties tested, and relationship of test environments. Desirable varieties in terms of yield performance and stability had been identified in wet season (i.e NSIC Rc212) and in dry season (i.e NSIC Rc118) that can be recommended to farmers for cultivation. The result of polygon biplot view was useful in determining varieties that are suitable in particular location or set of locations and it also graphically identified the presence of mega-environments. These information is useful in choosing for appropriate locations to conduct genotype/variety testing in the region in the future. Since a particular location can represent a group of environment that belongs to a mega-environment, it would be cost-effective to choose only the representative environment as trial site since same yield information can be obtained from these environments.

Keywords: adaptability, genotype x environment interaction, ggE biplot analysis, yield stability

1Philippine Rice Research institute negros Station, Cansilayan, Murcia, negros Occidental, Philippines*Corresponding author: e-mail -

517

Adaptability-stability of newly-released rice varieties

Asia Life Sciences 23(2) 2014

INTRODUCTIONThe average farm-sized holdings of rice farmers in developing countries are

generally small and farmers are growing crops in diverse environments usually with highly unpredictable and not well-understood factors within the target region. Variety recommendation for small homogenous areas within a target region is not a practical scheme specifically in rice due to institutional and administrative problems associated in handling large number of varieties. Hence, it would be appropriate for the farmers to use varieties adaptable to diverse rice environments. Recommendation of cultivars with wide adaptability is an important consideration on countries with diverse agro-ecological regions and seasons. Under such conditions, it is important to account the differential response of various rice varieties to varying environments on which case determine the importance of gE interaction in evaluating for stability and adaptability of different rice varieties over diverse environments and seasons (Simmonds, 1991). Correct assessment of varietal adaptability is an integral prerequisite to recommendation of varieties for cultivation.

Crop adaptability is commonly understood as wide or local adaptation as a result of high mean grain yield and yield stability across environments and seasons. On the other hand, yield stability of a cultivar is defined as consistency of yield response of variety(ies) across environments or seasons (Abeysiriwardena et al. 1991, Annicchiarico 2002). Varietal adaptability to environmental fluctuations is important for the stabilization of crop production on both region and season. information on the genotype x environment interaction is essential to the successful evaluation of stable genotypes (Umadevi et al. 2008). Multi-environment trials (MET) are conducted to evaluate the yield stability performance of different genetic materials under different environmental conditions (Delacy et al. 1996, Yan et al. 2000, Yan & Rajcan 2002). Oftentimes, genotypes subject to different environments show significant fluctuations on the yield responses. These changes are influenced by different environmental conditions commonly referred to as genotype-by-environment interaction (Allard & bradshow 1964). The action of gE interaction makes genetic progress difficult since it minimizes the association of phenotypic and genotypic values (Comstock & Moll 1963). Hence, gE interaction should be exploited through selection of superior genotypes on specific location or avoided by selecting genotypes with wide adaptability and stable yield response across diverse locations (Ceccarelli 1989). Recently, several methods in analyzing genotype x environment (gE) interaction and MET data to reveal patterns of gE have been reported. These methods include regression coefficient (Finlay and Wilkinson, 1963), sum of squared deviations from regressions (Eberhart & Russel 1966), stability of variance (Shukla 1972), coefficient of determination (Pinthus 1973) coefficient of variability (Francis & Kanneber 1978), and multiplicative main effects and multiplicative interaction (AMMi) (gauch & Zobel 1988, Zobel et al. 1988; gauch 1992, 2006) which have been commonly used. Another method proposed by Yan et al. (2000) proposed another method called ggE biplot analysis for graphical display of gE interaction pattern in MET data. ggE biplot is an effective method based on principal component analysis (PCA) which graphically

518

Palanog et al. 2014


displays gE in a two-way table and allows visual examination of the relationship interactions among test environments, genotypes, and gE interaction (Yan et al. 2000).

The final evaluation of varietal adaptability in a rice varietal testing program involves on-farm multi-location trials with the farmers’ participatory approach. Although several varieties are being evaluated in preliminary yield tests conducted in research fields, only few exhibited better performance with resistance to major pest and diseases are selected for on-farm trials and for further screening for wild adaptability that will eventually for recommendation. Thus, the main objectives of this study were to evaluate the stability performance of newly-released irrigated rice varieties under various environments; evaluate the yield performance of each variety in each location, and examine the relationships of varieties and the discriminating and representativeness of each test locations.

MATERIALS AND METHODSGenotypic materials used. Six newly-released rice varieties in the Philippines were evaluated on six different locations. Rice varieties tested are national Seed industry Council (nSiC)-approved and -released from 2010 to 2012 bred for irrigated lowland rice ecosystem. Particularly in this study, the variety trials were established in selected municipalities and cities in six provinces of Western Visayas, Philippines. Site description. The variety trials were conducted in selected cities and municipalities in the six provinces of Western Visayas Philippines which composed of Aklan, Antique, Capiz, guimaras, iloilo, and negros Occidental. Specifically, six sites were selected in Aklan, 3 sites in Antique, 6 sites in Capiz, 2 sites in guimaras, 7 sites in iloilo, and 10 sites in negros Occidental. Description of each site which includes climatic, edaphic, and topographic information is shown in Table 1.

Table 1. Climatic, edaphic, and topographic description of sites where the variety trial was conducted.Site Coordinates Soil Series Soil types Climate

TypeAklan Altavas 11o32’n 122o29’E bantog Clay iii Kalibo 11o42’n 122o22’E Unknown iii ibajay 11o49’n 122o10’E San Miguel Fine sandy

loam; loamiii

Makato 11o42’n 122o17’E Makato Clay iii n. Washington 11o39’n 122o26’E bantog Clay iii Tangalan 11o46’n 122o15’E Unknown iiiAntique barbaza 11o12’n 122o03’E San Miguel Fine sandy

loam; loamiii

Sibalom 10o47’n 122o01’E bantog Clay i

519



Patnongon 10o55’n 122o59’E Patnongon Sandy clay loam

i

Capiz Dumarao 11o16’n 122o41’E Unknown iii Panit-an 11o28’n 122o46’E Unknown iii Mambusao 11o26’n 122o36’E Unknown iii Panay 11o33’n 122o48’E Unknown iii ivisan 11o31’n 122o41’E Luisiana Clay iii Jamindan 11o24’n 122o30’E Alimodian Clay iiiGuimaras buenavista 10042’n 122o38’E guimaras Clay iii San Lorenzo 10039’n 122o43’E guimaras Clay iiiIloilo Tubungan 10047’n 122o18’E Unknown i Dingle 11o03’n 122o40’ E Sta. Rita Clay iii Sta. barbara 10o53’n 122o29’ E Sta. Rita Clay iii San Dionisio 11o21’n 123005’ E Sara Sandy clay

loamiii

banate 11o03’n 122o47’ E Unknown iii Miag-ao 10o39’n 122o14’ E Unknown iii WESViARC 11o00’n 122o40’E iiiNegros Occidental San Carlos City 10025’n 123o23’E La

CastellanaClay loam iii

Sagay City 10054’n 123o25’E Cadiz gravelly loam

iii

Hinigaran 10016’n 122o21’E Silay Sandy loam; loam; clay

iii

Cauayan 09058’n 123o37’E isabela Clay; sandy loam

i

Sipalay City 09045’n 122o24’E San Miguel Fine sandy loam; loam

i

PhilRice negros 10036’n 123o02’E Alimodian Clay iii Valladolid 10028’n 123o50’E Unknown iii bago City 10032’n 122o50’E bago Fine sandy

loam; Loam; sandy clay

loam

iii

Moises Padilla 10016’n 123o05’E La Castellana

Clay loam iii

520

Palanog et al. 2014


Establishment and management of field trials. Field experiments were laid out using in randomized complete block design (RCbD) with three replications established in two dry seasons (DS2010 & DS2011) and two wet seasons (WS2010 & WS2011) in six different locations. Field trials were managed under irrigated lowland condition with crop management employed was based on PalayCheck Sytem®: an integrated crop management system packaged from best farming practices and technologies in the Philippines. Collection of grain yield data. grain yield is the most important parameter gathered in this study. grains were harvested at physiological maturity or 80-85% of the panicles turned into golden yellow and the panicles at the base were already at hard dough stage. Harvested grains were threshed and weighed for yield data. Moisture content was adjusted to 14% in computing for the grain yield data. Statistical analysis. The grain yield data were analyzed using combined analysis of variance (AnOVA) to measure the effects of genotype (g), environment (E), and genotype by environment interaction (gE). The effect gE interaction was further analyzed using a graphically presentation using ggE biplot analysis generated by ggE biplot software (Yan, 2001). The graphs generated were used as basis for: (a) determining the relationships among environments tested;(b) discriminating ability of test environment; (c) representativeness of environments; (d) mean performance and stability of varieties; (e) comparison among genotypes, and (f) which-won-where.

RESULTS AND DISCUSSIONSCombined analysis of variance (ANOVA) over sites for grain yield. The combined analysis of variance showed highly significant results on all sources of variation (p<0.01) as shown on Table 1. Environment or location effect is highly significant and accounts for about 13% of the total sum of squares (TSS). The gE interaction main effecs accounts for 41% of the TSS more than twice the genotypic main effects. The large gE interaction effect relative to genotype implies that environments might be divided into mega-environments and differential response of genotypes across locations is expected (Yan & Kang 2003). The presence of mega-environments was confirmed by ggE biplot analysis and was discussed on the succeeding topics. The significant gE effects of grain yield rice genotypes to different environments indicate that genotypes exhibit both additive and crossover types of interaction. The presence of large magnitude of gE interaction complicates the selection for best performing and stable genotypes as gE interaction reduces the usefulness of genotypes since it minimizes the association between genotypic and phenotypic values (Comstock & Moll 1963). Proper interpretation of gE interaction is vital in plant breeding programs that involve multi-location testing.

521



Table 1. Combined analysis of variance (AnOVA) over sites (n=6).

Source of variation Degrees of freedom Sum of squares Mean squares

Location (L) 5 29.60867 5.921733**Reps within site 18 18.488 1.024889**Variety (V) 5 34.48088 6.89177**VxL 25 90.212001 3.6084**Pooled error 90 220.3455 17.98007

**highly significant at (p ≤ 0.01)

Relationship among test environments. Result of ggE biplot analysis (Figure 1) summarizes the interrelationships of different test environments for both wet and dry season. it is based on gxE table scaling (scaling = 1), on an environment center (centering = 2), and environment-metric preserving (SVP =2). The biplot graph explained 87.2% and 86.8% of the total variation of the environment-centered gxE table for wet season (Figure 1a) and dry season (Figure 1b), respectively. The angle of vectors between environments gives an estimate of their correlation coefficient. The presence of the obtuse angle indicates strong negative correlation and large gE crossover while acute angle signifies positive correlation between environments and right angle means no correlation (Yan & Tinker 2006).Wet season. The acute angle distance of the vectors of Aklan, guimaras, and negros Occidental on Figure 1a show that these environments are positively correlated while the obtuse angles of Antique, iloilo, and Capiz against Aklan, guimaras and negros Occidental denote negative correlation between these environments. The biplot analysis revealed two distinct groups of environments: Aklan, guimaras, and negros Occidental group and iloilo, Capiz and Antique group. Environments belonging to the same group will yield the same information on the performance of the varieties.Dry season. Figure 1b shows that Antique, Capiz, and Aklan have acute angle distances, implying that these environments are relatively similar and are positively correlated while environments such as guimaras, iloilo and negros Occidental are relatively different environments shown by their obtuse angle distance. The presence of obtuse angle among environments indicates a strong crossover gE and implies that gE is moderately large. The figure also identifies four major environment groups: Antique, Capiz and Aklan belong to a group whereas guimaras, negros Occidental and iloilo are three dissimilar environments. Environments that belong to the different group are expected to have different discriminating ability.

522

Palanog et al. 2014


Figure 1. interrelationships of different sites of variety trial in Western Visayas, Philippines conducted in wet season (a) and dry season (b).

Discriminating ability and representativeness of test environments. The discriminating ability of the environments can be measured by presence of concentric circle. The length of the vectors which is proportional to the standard deviation within the respective environment is the measure of the discriminating ability of the environments. On the other hand, Average-Environment Axis or average tester axis determines the degree of representing ability of the environments. A test environment which has a smaller angle with the AEA is more representative of other environments. Wet season. Among the six environments, Aklan province is the most discriminating environment and more likely the representative of the all the environments on the basis of the vector length and proximity to average environment axis (Figure 2a). Discriminating ability of other environments is relatively similar, with guimaras exhibiting the shortest vector hence, the least discriminating of all environments. The test environments that are consistently non-discriminating will provide little information on the genotypes and should not be used as test environments.

Meanwhile, Capiz and Aklan provinces can be the ideal representative sites as shown by their acute vector angle with the AEA while negros Occidental and Antique are the least representative sites due to their obtuse vector angles with the AEA (Figure 2a). Discriminating but not representative environments such as negros Occidental and Antique are useful in selecting for specifically adapted genotypes if the target environments are divided into mega-environments. non-discriminating and non-representative environment like guimaras are less useful because it provides less discriminating information about the genotypes. Discriminating and representative environment like Aklan province is useful in selecting for widely adaptable varieties

a b

523



Dry season. based on the length of vectors of the different sites, iloilo is the most discriminating site while Capiz is the least discriminating site among the sites (Figure 2b). Thus, it is useful to evaluate genotypes to the former than the latter. Relative to the Average-environment Axis (AEA), Aklan is most ideal representative of all sites since it has the least angle distance to the AEA whereas negros Occidental and guimaras are the least representative of all the sites as shown by the obtuse angle distances from the AEA. Aklan is the most appropriate test environments compared to others sites owing to its representativeness and discriminating ability hence; it would be an ideal site for testing varieties during the dry season. Discriminating and non-representative site like iloilo is best in evaluating specifically-adapted varieties if sites are divided in mega-environments.

Figure 2.Discriminating ability and representativeness of various locations of variety trial in wet season (a) and dry season (b).

Relationships among varieties.Wet season. The distance between genotypes approximates their Euclidean distance which measures their overall dissimilarity. Varieties with vector angles close to each other or with acute angle distance are considered to be similar in their performances ex. nSiC Rc224 and nSiC Rc222 while varieties with larger or far angle distances such as nSiC Rc160 and nSiC Rc224 are dissimilar varieties due to difference in their mean yield and/or in interaction with the environment (gE). nSiC Rc214 is very proximate to the origin of the biplot which represents a “virtual genotype” that assumes the average value thus; this variety has the mean performance similar to the overall average performance. The variety could have a zero contribution to both g and gE. On the other hand, varieties having longer vector such as nSiC Rc160 have higher g and gE contributions which could be the best or the poorest among the varieties in terms of yield performance. Varieties that form acute angle with each other (nSiC Rc224, nSiC 222, nSiC Rc216, and nSiC 214) have similar response in certain environments while varieties forming obtuse

a b

524

Palanog et al. 2014


angle (nSiC Rc160 and nSiC 224 and nSiC Rc160 and nSiC Rc212) respond differently in environments.Dry season. Top and poor performing varieties, respectively can be distinguished by longer vectors. This is true with nSiC Rc160 which one of the best performing variety whereas nSiC Rc214 is the poorest performing among varieties. Varieties with acute angle distance to each other are considered similar in terms of performance and response such as nSiC Rc160, nSiC Rc118 and nSiC Rc212. On the other hand, nSiC Rc216 and nSiC Rc118, and nSiC Rc214 and nSiC Rc222 have obtuse angle distance denoting the dissimilarity on the responses of these varieties to different environments due to difference in their mean performance or gE interactions.

Figure 3. Relationship among varieties in terms of yield performance and gE interaction in wet season (a) and dry season (b).

Mean performance and stability analysis of different varieties. Figure 4 illustrates the ranking of six rice varieties based on their mean yield and stability performance. genotype-metric preserving (SVP = 1) was used since this is appropriate for evaluation of genotypes/varieties. The average-environment coordination (AEC) or single-arrowed line denotes to the direction of high mean yield across environments while the AEC ordinate or the double-arrowed line determines the stability of the varieties (Yan & Tinker 2006). Varieties located near the concentric circle have high mean yield. Vector line of each variety which is perpendicular to the AEC indicates the stability of the varieties. Regardless of the direction of vectors, varieties with longer vector are less stable compared to varieties with shorter vectors. Wet season. nSiC Rc212 is the best performing variety in term of mean yield performance followed nSiC Rc214>nSiC Rc118>nSiC Rc216 while nSiC Rc160 is the poorest in terms of mean performance (Figure 4a). Stability of the performance of varieties relative to the AEC showed that nSiC Rc212 is the most stable variety while nSiC Rc160 is the most unstable variety owing to low and high gE interaction, respectively. Consequently, nSiC Rc212 is the most ideal variety for wet season in all environments due to high yield mean yield and high yield stability.

a b

525



Dry season. Top performing varieties on the basis of mean yield were nSiC Rc212 and nSiC Rc118 while poor performing varieties where nSiC Rc222<Rc214<Rc116 as illustrated by the figure relative to the Average-Environment Coordinate. nSiC Rc224 mean yield is similar to the average mean yield of varieties since it is located at the origin of the biplot. Comparing the stability of varieties in reference with their vector length, nSiC Rc118 can be regarded as the most stable variety followed by nSiC Rc224 however the latter variety have poor performance in terms of mean yield. nSiC Rc214 followed by nSiC Rc222 was the most unstable varieties because of their longer vectors. nSiC Rc118 is the ideal variety for all environments based on stability and mean yield performance.

Figure 5. Mean yield and stability performance of six newly-released varieties across six sites in Western Visayas, Philippines in both wet season (a) and dry season (b).

Which-won-where. Figure 6 represents the polygon view of biplot, one of the best features of the ggE biplot that faciliatate the visualization of the interaction patten between environment and genotypes/varieties (Yan & Kang 2003). it has the ability to show the which-won-where pattern of genotype by environment interaction. genotypes located at the vertex of the polygon are either the best or the poorest performing varietes in one or more environments (Yan & Tinker 2006). Perpendicular lines are equality lines that divide the biplot into sectors and facilitate visual comparison of varieties. Top peforming variety for each sector is one that is located on the respective vector.Wet season. The polygon biplot revealed three sections of environment. Antique, iloilo, and Capiz are grouped in one section where best performing varieties are nSiC Rc216>nSiC Rc222>nSiC Rc224>nSiC Rc214. Thus, nSiC Rc214 is an ideal variety with high yield and adaptabilty that can be recommended to provinces of Antique, iloilo and Capiz. guimaras and negros Occidental belong to the same section with nSiC Rc160 as outstanding variety. Lastly, Aklan is separated in different section with nSiC Rc212 as top performing variety. Considering the high

a b

526

Palanog et al. 2014


discriminating ability of guimaras as test environment proves that nSiC Rc212 is an ideal variety for specific adaptability to the said environment.

Dry seasonFigure 6b illustrates the specific variety that performs best on an environment

or group of environments which can be located at the vertex of the polygon. in this case, best performing varieties in the respective environments where nSiC Rc222 in guimaras, nSiC Rc214 in negros Occidental, and nSiC Rc212 in iloilo. Meanwhile, nSiC Rc118 was considered as the best performing and widely adaptable variety in Antique, Capiz, and Aklan. Environments were subdivided into three sections of mega-environments. Antique, Capiz, and Aklan belong to different a mega-environment while guimaras, iloilo, and negros Occidental each belong to three distinct environments.

Figure 6.Polygon biplot view of genotype-environment interaction of newly-released rice varieties in six test location in Western Visayas, Philippines in wet season (a) and dry season (b)

CONCLUSIONResults of the combined analysis of variance indicates that the performance

of six newly-released rice varieities were highly influenced by gE interaction. The main effects of gE interaction that is twice higher than the genotypic main effects, supports the assumption for the presence of high gE interaction. Meanwhile, the results of the ggE biplot analysis allows the graphical presentation of the effect and magnitude of gE interaction, examines the relationships among genotypes among test environments, and determines the variations on the performance of the genotypes in various environments (Farshadfar et al. 2012). Large gE crossover among environments was identified with highly variable correlation relationship that exists among environments connoting the diversity of the test environments. Desirable varieties in terms of yield performance and stability had been identified in both wet season (i.e nSiC Rc212) and dry season (i.e nSiC Rc118) that can be recommended to farmers for cultivation. Seasonal variation of varietal performance was revealed such in the case of nSiC Rc160 who performs better during the wet

a b

527



season but not on the dry season. The result of polygon biplot view was useful in determining varieties that are suitable in a particular location or set of locations and it also graphically identified the presence of mega-environments. These information is useful in choosing for appropriate locations to conduct genotype/variety testing in the region in the future. Since some of the locations can represent a group of environment that belongs to a mega-environment, it would be cost-effective to choose only the representative environment as trial site since same yield information can be obtained from these environments.

LITERATURE CITEDAllard, R.W. and A.D. bradshaw. 1964. implication of genotype-environmental interaction

in applied plant breeding. Crop Science 5: 503-506.Cecarelli, S. 1989. Wide adaptation. How wide. Euphytica 40: 197-205.Comstock, R.E. and R.H. Moll. 1963. genotype x Environment interactions. Symposium

on Statistical genetics and Plant breeding. national Academy Science Research Council, Washington, D.C., pp:164-196.

Delacy i.H., K.E. basford, M. Cooper and J.K. bull. 1996. Analysis of environment trials – an historical perspective. In: Cooper, M. and g.L. Hammer (Eds.). Plant Adaptation and Crop improvement, CAb international, U.K.

Eberhart, S.A. and W.A. Russel. 1966. Stability parameters for comparing varieties. Crop Science 6: 36-40.

Farshadfar, E., R. Mohammadi, M. Aghaee and Z. Vaisi. 2012. ggE biplot analysis of genotype x environment interaction in wheat-barley disomic addition lines. Australian Journal of Crop Science 6(6): 1074-1079.

Finlay, K.W. and g.n. Wilkinson. 1963. The analysis of adaptation in a plant breeding programme. Australian Journal of Agricultural Research 14: 742-754.

Francis, T.R. and L.W. Kannenberg. 1978. Yield stability studies in short season maize. i. A descriptive method for grouping genotypes. Canadian Journal of Plant Science 58: 1029-1034.

gauch, H.g. 1992. Statistical Analysis of Regional Yield Trials: AMMI Analysis of Factorial Designs. Elsevier, Amsterdam, The netherlands.

gauch, H.g. and R.W. Zobel. 1988. Predictive and postpredictive success of statistical analyses of yield trials. Theoretical and Applied genetics 76: 1-10.

Pinthus, J.M. 1973. Estimate of genotype value: A proposed method. Euphytica 22: 121-123.Shukla, g.K. 1972. Some statistical aspects of partitioning of genotype-environmental

components of variability. Heredity 29: 237-245.Yan, W., L.A. Hunt, Q. Sheng and Z. Szlavnics. 2000. Cultivar evaluation and

megaenvironment investigation based on the ggE biplot. Crop Science 40: 597-605.Yan, W. and M.S. Kang. 2003. ggE biplot Analysis: A Graphical Tool for Breeders,

Geneticists and Agronomists. 1st Edn., CRC Press LLC., boca Roton, Florida, 271 p.Yan, W. and n.A Tinker. 2006. biplot analysis of multi-environment trial data: Principles

and applications. Canadian Journal of Plant Science 86: 626-645. Zobel, R.W., M.J. Wright and H.g. gauch. 1988. Statistical analysis of a yield trial. Agronomy

Journal 80: 388-393.Yan, W. and n.A. Tinker. 2006. biplot analysis of multi-environment trial data: Principles

and applications. Canadian Journal of Plant Science 86: 623-645. Asia Life Sciences Beyond Excellence©

©Rushing Water Publishers Ltd., Philippines 2014

528

Palanog et al. 2014


Beyond Excellence©

81 Governor F.T. San Luis Avenue, Masaya, Bay 4033Laguna, Philippines

Celfone nos. (063) (049) 0915-360-4660; 0921-374-9752Landline telephone no. (063)(049) 501-2957

e-mails: [email protected]@gmail.com

http://journals.uplb.edu.ph/index.php/ALS

©Rushing Water Publishers Ltd., Philippines 2014

The papers published in Asia Life Sciences are indexed in theBiological Abstracts, CAB Abstracts, CAB Global Health,

Zoological Record, SciSearch®/Science Citation IndexExpanded, Journal Citation Reports/Science Edition, BIOSIS

Previews, ISI Web of Science®, ISI Web of Knowledge®and are covered by the

Thomson Reuters-Institute for Scientific Information (ISI), USAand CABI, Wallingford, Oxon, UK.

Asia Life Sciences has an Impact Factor of 0.259.

Asia Life Sciences is a recipient of the Journal AccreditationAward of the Commission on Higher Education (CHED),

Republic of the Philippines (2010-2015).

Printed on acid-free papers

Printed in the Philippines Adaptability and stability analysis of newly-released rice varieties...

Documents

Transcript of Printed in the Philippines Adaptability and stability analysis of newly-released rice varieties...