Nonparametric Stability Statistics Research Articles

Exploring Resistant Sources of Chickpea against Fusarium oxysporum f. sp. ciceris in Dryland Areas

Fusarium wilt is a fungal disease that has a significant impact on chickpeas worldwide. This study examined the response of 58 chickpea genotypes to Fusarium wilt. The experiment was conducted over two growing seasons at the Sararoud and Maragheh research stations at the Drylands Agricultural Research Institute of Iran. Genotype resistance was screened through wilt incidence records and nonparametric stability statistic evaluation. The identified resistant genotypes were then evaluated in the greenhouse for their response to four isolates of Fusarium oxysporum f. sp. ciceris (races 1/BC, 2, 4, and 6). Out of 58 genotypes, 32 exhibited moderate resistance, while 24 showed strong resistance abilities. Under warmer conditions, disease severity was higher, with scores at the Sararoud location being higher than those at the Maragheh location. Of the total genotypes across all locations and years, 41.4% were resistant, 55.17% were moderately resistant, 1.72% were susceptible, and 1.72% were highly susceptible. The nonparametric stability measures S(1), S(2), and S(3) identified FLIP 05-42C and FLIP 05-43C as stable and resistant genotypes. The study found that Azad/Hashem K3 was stable based on the non-parametric stability measure S(6). Other resistant genotypes were identified using stability parameters NP(1), NP(3), and NP(4), while FLIP 05-104C was identified by NP(2). The genotypes selected by nonparametric stability parameters showed resistance against at least two Fusarium oxysporum f. sp. ciceris races. The screening method and nonparametric stability statistics used in this study were effective in identifying sources of resistance to Fusarium oxysporum f. sp. ciceris.

ОЦЕНКА СТАБИЛЬНОСТИ УРОЖАЙНОСТИ ЯРОВОГО ЯЧМЕНЯ

The purpose of research is to determine the share of influence of factors in the overall variability of barley yield and assess stability using parametric and non-parametric statistical methods in the conditions of the south of the Moscow Region. According to the results of the study (2020–2022), significant contributions to yield variability were identified: environmental conditions – 81.9 %; genotypic characteristics – 7.4 and genotype × environment interaction – 8.2 %. 7 samples (280.7–345.2 g/m2) were classified as excee¬ding the standard. Evaluation by parametric (bi – regression coefficient; S2dᵢ – deviation from regression; θi – average variance component; θ(i) – GE variance component; Wi2 – environmental valence; σi2 – stability variance; CV – coefficient of variation) and nonparametric (S(1,2,3,6) – rank statistics; NP(i) – non-parametric stability statistics; KR – rank sum) stability indicators allowed us to identify differences in the response of the sample under study to environmental conditions. A high correlation was noted between S(3) and NP(4) (r = 0,90), S(1) and S(2) (r = 0,96), S(6) and NP(4) (r = 0,98), Wi2 and σi2, (r = 1,00), Wᵢ2 and θᵢ (r = 1,00), σ2ᵢ and θᵢ (r = 1,00), Wᵢ2 and θ(i) (r = –1,00), σ2ᵢ, and θ(i) (r = –1,00), θᵢ and θ(i) (r = –1,00). Relationship with productivity in such indicators as S(1), S(2), S(3), NP(1), Wᵢ2, σ2ᵢ, S2dᵢ, CV, θᵢ, θ(ᵢ) was weak, the greatest association was noted with bᵢ (r = 0,60), NP(3) (r = –0,67), NP(4) (r = –0,67), S(6) (r = –0,69), KR (r = –0,71), NP(2) (r = –0,78). Samples with a combination of stability and high yield were identified: Alei (k-31363, Russia), Maximus (k-31366, Russia), Austris (k-31368, Latvia), Povolzhsky Luch (k-31392, Russia).

Grain yield stability analysis using parametric and nonparametric statistics in oat (Avena sativa L.) genotypes in Ethiopia

AbstractBackgroundThe performance of oat genotypes differs across environments due to variations in biotic and abiotic factors. Thus, evaluation of oat genotypes across diverse environments is very important to identify superior and stable genotypes for yield improvement.MethodsThe study aimed to assess the interaction (genotype‐by‐environment interaction; GEI) effect and determine the stability of grain yield in oat (Avena sativa L.) genotypes in Ethiopia using parametric and nonparametric stability statistics. Twenty‐four oat genotypes were evaluated in nine environments using a randomized complete block design replicated three times.ResultsThe pooled analysis of the variance of grain yield showed significant variations among genotypes, environments, and their interaction effects. Significant GEI revealed the rank order change of genotypes across environments. The environment main effect captured 44.62% of the total grain yield variance, while genotype and GEI effects explained 28.84% and 26.54% of the total grain yield variance, respectively. The grain yield stability was assessed based on 12 parametric and two nonparametric stability statistics. The results indicated that genotypes with superior grain yield‐ showed stable performance on the basis of the stability parameters of the genotypic superiority index (Pi), the Perkins and Jinks adjusted linear regression coefficient (Bi), and the yield stability index (YSI), indicating that selection using these stability parameters would be efficient for grain yield enhancement in oat genotypes. Spearman's rank correlation coefficients also showed that the stability parameters of Pi, Bi, and YSI had a significant positive association with grain yield. However, grain yield had an inverse correlation with the stability parameters of standard deviation, deviation from regression , the Hernandez desirability index (Dji), Wricke ecovalence (Wi), the Shukla stability variance (σi2), the AMMI stability value (ASV), and environmental variance , indicating that oat genotype selection using these stability parameters would not be efficient for yield enhancement because these stability parameters favor low‐yielding genotypes more, compared to high‐yielding ones.ConclusionsTherefore, G5, G8, G11, G12, G14, G16, G17, G19, and G22 genotypes were adaptable in all nine environments based on stability parameters of Pi, Bi, and YSI, and selection of these superior genotypes would improve grain yield in oat genotypes. However, the validity of this result should be confirmed by repeating the experiment in the same environments over two or more years.

Integrating BLUP, AMMI, and GGE Models to Explore GE Interactions for Adaptability and Stability of Winter Lentils (Lens culinaris Medik.).

Lentil yield is a complicated quantitative trait; it is significantly influenced by the environment. It is crucial for improving human health and nutritional security in the country as well as for a sustainable agricultural system. The study was laid out to determine the stable genotype through the collaboration of G × E by AMMI and GGE biplot and to identify the superior genotypes using 33 parametric and non-parametric stability statistics of 10 genotypes across four different conditions. The total G × E effect was divided into two primary components by the AMMI model. For days to flowering, days to maturity, plant height, pods per plant, and hundred seed weight, IPCA1 was significant and accounted for 83%, 75%, 100%, and 62%, respectively. Both IPCA1 and IPCA2 were non-significant for yield per plant and accounted for 62% of the overall G × E interaction. An estimated set of eight stability parameters showed strong positive correlations with mean seed yield, and these measurements can be utilized to choose stable genotypes. The productivity of lentils has varied greatly in the environment, ranging from 786 kg per ha in the MYM environment to 1658 kg per ha in the ISD environment, according to the AMMI biplot. Three genotypes (G8, G7, and G2) were shown to be the most stable based on non-parametric stability scores for grain yield. G8, G7, G2, and G5 were determined as the top lentil genotypes based on grain production using numerical stability metrics such as Francis's coefficient of variation, Shukla stability value (σi2), and Wrick's ecovalence (Wi). Genotypes G7, G10, and G4 were the most stable with the highest yield, according to BLUP-based simultaneous selection stability characteristics. The findings of graphic stability methods such as AMMI and GGE for identifying the high-yielding and stable lentil genotypes were very similar. While the GGE biplot indicated G2, G10, and G7 as the most stable and high-producing genotypes, AMMI analysis identified G2, G9, G10, and G7. These selected genotypes would be used to release a new variety. Considering all the stability models, such as Eberhart and Russell's regression and deviation from regression, additive main effects, multiplicative interactions (AMMI) analysis, and GGE, the genotypes G2, G9, and G7 could be used as well-adapted genotypes with moderate grain yield in all tested environments.

Comprehensive Stability Analysis of Wheat Genotypes through Multi-Environmental Trials

In rainfed areas, due to variable environmental factors, improving the yield stability of the introduced cultivars along with increasing yield should be considered. The main aim of this study is to obtain high-yield wheat genotypes that are stable and adaptable to cold climatic conditions in Iran. For this purpose, 25 wheat genotypes were evaluated in a randomized complete blocks design with three replications during three cropping seasons (2013-2016) under supplementary irrigation and rainfed conditions. PBSTAT-GE software was used for genotype × environment interaction (GEI) analysis and comprehensive sustainability analysis. The results showed that G5, G14, G16 and G18 genotypes had good stability and general adaptation based on parametric and non-parametric stability statistics. Combined analysis of variance based on the Additive Main Effect and Multiplicative Interaction (AMMI) model showed that GEI is significant in the term of grain yield. Also, the ratios between the sum of squares G, GE and IPC1 showed that the AMMI is suitable for data analysis. GGE biplot analysis identified five mega-environments (MEs), in which ME I including E1, E2, E3, E4, E5, E6, and G7, G5, G14, G13, G16, G18, G20 being the superior ME I genotypes. According to AMMI and GGE biplote stability methods, lines G20, G18, G13, G16, G14 and Saein cultivar (G5) can be considered as desirable genetic resources in wheat production programs under variable environments in Iran, due to having the appropriate combination of yield and stability.

Comparison of Ammi, Parametric and Non-Parametric Models in Identifying High-Yielding and Stable Oilseed Rape Genotypes

One of the complex issue in the way of releasing new high-yielding and stable oilseed rape ‎cultivars is genotype by environment interaction (GEI) which reduce selection efficiency. In ‎the current study, parametric and non-parametric statistics as well as the AMMI model have ‎been compared to identify the best stability models to clarify GEI complexity. The ‎experiment has been conducted in the warm regions of Iran including; Gorgan, Sari, Zabol, ‎and Hajiabad during two cropping seasons (2016-2017 and 2017-2018) for 16 genotypes in a ‎randomized complete block design with three replications. The AMMI analysis of variance on ‎grain yield showed the significant effects of genotype, environment, and the interaction ‎effects of GEI on yield. Based on the AMMI ANOVA, the major contribution of GEI was ‎captured by the first and second interaction principal component axes (IPCA1 and IPCA2) ‎which explained 34.29% and 29.81% of GEI sum of the square, respectively. Additionally, ‎Different parametric and non-parametric stability methods including; bi, S2di, CVi, W2i, σ2i, Pi, ‎Si(1), Si(2), Si(3), Si(6), Npi(1), Npi(2), Npi(3), Npi(4), KR and TOP have also investigated. Based on ‎AMMI, parametric, and non-parametric stability statistics, genotypes G2 (SRL-95-7) and G9 ‎‎(SRL-95-16)‎‏ ‏were selected as the stable and high-yielding genotypes. Likewise, Principal ‎component analysis based on rank correlation matrix enabled us to distinguish high-yielding ‎genotypes to stable (high-yielding genotypes in various environments) and unstable (high-‎yielding genotypes in low-yielding environments) ones. Furthermore, a significant Spearman ‎correlation was observed between yield mean and GSI, Pi, Si(3), Si(6), Npi(3), Npi(4), and KR. ‎Therefore, different efficient strategies were identified in this study‏ ‏and since we looked up ‎high-yielding and stable genotypes, G2 (SRL-95-7) and G9 (SRL-95-16)‎‏ ‏were finally ‎selected.‎

Non-parametric Stability Analysis in Durum Wheat (Triticum durum Desf.) Genotypes Growing under Semi-arid Conditions

Field experiments were carried out in National Institute of Agronomic Research of Algeria INRAA- station of Setif, Algeria where grain yield of 10 durum wheat genotypes tested during 4 cropping seasons from 2009 to 2013 and sowing in the same period in December .The objectives were to determine stable genotypes based on non-parametric stability indices. The results of the Thennarasu (NP) as following: Si(1) (varied from 2 to 5), Si(2) (varied from 3 to 17), Si(3) (varied from 1.2 to 9.27) and Si(6) (varied from 0.67 to 2.55) based on these methods the genotype Mexicali75 is considered to be most stable and had highest grain yield. Based on NP1, NP3 and NP4 the genotype Mexicali75 is considered to be most stable and had highest grain yield, the genotype Hoggar had low values for the index NP2 we can considered more stable but with moderate grain yield. In addition, the results of the methods of Huehn demonstrate that the genotype Mexicali75 with the lowest values was identified as desirable. The results of Spearman’s rank correlation coefficients between mean yield and the non-parametric stability statistics are shown in Table 4. The mean grain yield as well as negatively and significantly correlated with NP2, NP3 and NP4 and insignificant negative correlation with NP1. In conclusion, according to results of these different non-parametric stability measurements, genotype Mexicali75 is recommended for commercial release as a favorable durum wheat genotype for the semi-arid condition.

Dissection of genotype-by-environment interaction and yield stability analysis in barley using AMMI model and stability statistics

BackgroundBarley is one of the most important cereal crops with considerable tolerance to various environmental stresses, which can maintain its productivity well in marginal croplands. The selection of stable and high-yielding barley genotypes and ideal discriminative locations is an important strategy for the development of new cultivars in tropical climates. Different statistical methods have been developed to dissect the genotype-by-environment interaction effect and investigate the stability of genotypes and select discriminative environments. The main objective of the present study was to identify high-yielding and stable barley genotypes and testing environments located in the tropical regions of Iran using 23 parametric and nonparametric stability statistics. In the present study, the grain yield stability in nineteen barley genotypes was investigated across five different locations over two consecutive years (2018–2020).ResultsThe additive main effects multiplicative interaction (AMMI) analysis showed that environments (E), genotypes (G) and GE interaction effects were significant for grain yield. Using Spearman’s rank correlation analysis, a pattern map developed simultaneously for assessing relationships between grain yield and stability statistics and clustering of them, which allowed identifying two main groups based on their stability concepts. The biplot rendered using the weighted average of absolute scores (WAASB) and mean grain yield identified superior genotypes in terms of performance and stability. Among test environments, Darab, Gonbad and Zabol showed a high discriminating ability and played the highest contribution in creating GEI. Hence, these regions are suggested as discriminative sites in Iran for the selection of high-yielding and stable barley genotypes.ConclusionAs a conclusion from this research, all stability statistics together identify G10 and G12 as the superior barley genotypes; these genotypes could be released as commercial cultivars in tropical regions of Iran.

Genotype × environment interaction and stability analyses of grain yield in rainfed winter bread wheat

Abstract The genotype × environment (GE) interaction analysis is fundamental in crop breeding programs to guide selection and for recommendation of high performing and stable genotypes for breeding objectives. This study aimed at quantifying the GE interaction effects and determines grain yield stability among winter bread wheat genotypes under rainfed conditions of Iran. Twenty-four winter wheat genotypes were evaluated under nine test locations using a randomized complete blocks design with four replications during three cropping seasons (2019–21). The additive main effects and multiplicative interaction (AMMI) model and several parametric and nonparametric stability statistics were applied for analysis of grain yield data collected from the experiments. AMMI analysis of variance for grain yield revealed significant effects (p < 0.01) for genotype, environment, and GE interaction. The environment was the main source of variation and accounted for 83.5% of the total yield variation, followed by GE (6.5%) and genotype (1.0%) effects. The AMMI biplot analysis indicated the genotypes G3, G23, G22, G10, and G19 as high yielding with stability performance across environments. Genotypes G14, G13, G20, and G9 showed large positive interaction with the environments featuring the highest rainfall during growing season, while genotypes G7, G6, and G21 had a large positive interaction with environments with low rainfall. Spearman’s rank correlation analysis revealed that the AMMI stability value, Shukla’s stability variance (σ2i), Wricke’s ecovalence (W2i), coefficient of determination (R2i), variance in regression deviations (S2di), and nonparametric statistic of S2(i) were not correlated with mean yield in tested genotypes, showing they are related to static/biological concept of stability. In contrast, the genotypic superiority index (Pi) and regression coefficient (bi) were significantly correlated (p < 0.01) with mean yield and corresponded to dynamic/agronomic concept of stability. These findings suggest that selection of genotypes should be considered based on selection objectives of using the various stability parameters described here. In conclusion, the selected genotypes in this study should be recommended as new cultivars or parental lines for grain yield and stability improvement under rainfed conditions of Iran or similar agro-ecologies.

Stability Analysis of Seed Yield of Ethiopian Caraway (Trachyspermum ammi L. Sprague ex Turrill) Genotypes in Multi-Environment Trials

Twelve Ethiopian caraway genotypes were evaluated in eight environments in Ethiopia during 2014 and 2015 under rain fed condition. The field experiment was laid out using randomized complete block design with three replications. The objective of this experiment was to select high yielding and stable Ethiopian caraway genotypes with nonparametric methods. Combined analysis of variance was performed and ten rank-based nonparametric stability parameters were measured. A pooled analysis variance for seed yield indicated that there were highly significant (P<0.001) differences for main effects of genotype, environment and their interaction. The results of principal component analysis revealed that the stability statistics and yield were classified into three groups and related to two contrasting concept of stability. In this study, TOP and rank-sum were found to be useful measures for simultaneously selecting high yield and stable cultivars. These measures selected Gondar 027-2001 and Gondar 023-2000 that gave 2254.7 Kg ha-1 and 2186.1 Kg ha-1 seed yield respectively, as stable and the National Variety Release Committee has released these genotypes as the first Ethiopian caraway varieties for production in 2017. Keywords : Ethiopian caraway, nonparametric stability statistics DOI: 10.7176/FSQM/97-06 Publication date: May 31 st 2020

Metan: An R package for multi‐environment trial analysis

Abstract Multi‐environment trials (MET) are crucial steps in plant breeding programs that aim at increasing crop productivity to ensure global food security. The analysis of MET data requires the combination of several approaches including data manipulation, visualization and modelling. As new methods are proposed, analysing MET data correctly and completely remains a challenge, often intractable with existing tools. Here we describe the metan R package, a collection of functions that implement a workflow‐based approach to (a) check, manipulate and summarize typical MET data; (b) analyse individual environments using both fixed and mixed‐effect models; (c) compute parametric and nonparametric stability statistics; (d) implement biometrical models widely used in MET analysis and (e) plot typical MET data quickly. In this paper, we present a summary of the functions implemented in metan and how they integrate into a workflow to explore and analyse MET data. We guide the user along a gentle learning curve and show how adding only a few commands or options at a time, powerful analyses can be implemented. metan offers a flexible, intuitive and richly documented working environment with tools that will facilitate the implementation of a complete analysis of MET datasets.

Evaluating leaf rust reactions of pure bread wheat landrace lines using non-parametric statistics

This research aims to assess genotype × environment interactions (G × E) of the leaf rust (Puccinia triticina) reactions of 29 Turkish landrace-derived pure lines over seven environments. Field experiments were conducted in Canakkale, Edirne and Samsun (Turkey) in three consecutive growing seasons from 2011-2012 to 2013–2014 under natural conditions. Leaf rust reactions of genotypes were recorded in accordance with the Modified Cobb scale. Genotype stabilities assessed by non-parametric stability statistics. Furthermore, a Principal Component Analysis (PCA) biplot of non- parametric stability statistics were used to compare their capability for capturing both stability and leaf rust reactions of genotypes in the given dataset. PCA Biplot results showed that non-parametric parameters of Thennarasu and Percentage Availability (PA) showed a positive association with mean disease severity while Rank Means (RM) were negatively associated. Non-parametric stability analyses revealed that Genotypes 29, 25 and 15 were the most stable pure lines when only 25 and 15 could be considered as resistant-stable. In order to determine both stability and leaf rust resistance in bread wheat, PA and RM are recommended.

Comparison of Non-parametric Stability Statistics for Selecting Stable and Adapted Soybean Genotypes under Different Environments

The objectives of this study were to investigate the comparison among non-parametric stability statistics and to evaluate seed yield stability of the sixteen soybean genotypes across four locations during the 2016, 2017 and 2018 growing seasons in Egypt. All trials were laid down in a randomized complete block design (RCBD) with three replications. The AMMI analysis showed ahighly significant effect of genotype (G), environment (E) and G x E interaction (GEI). The major contributions to treatment sum of squares were GEI, followed by G and E. The AMMI analysis also partitioned the total GEI component into eleven PCAs and Residual. The first eight PCAs were highly significant and accounted for about 99.56% of the total GEI. Based on the static and dynamic concepts, the results of spearman’s rank correlation and PCA showed that stability measures could be classified into three groups. The non-parametric stability statistics i.e., YSi, KR, TOP, RSM and δgy related to the dynamic concept and strongly correlated with mean seed soybean yield of stability. While, the other non-parametric stability statistics (Si(1) ,Si(2) ,Si(3) and Si(6),NPi(1) ,NPi(2) ,NPi(3) and NPi(4) , δr, MID, LOW) represented the concept of static stability, which were influenced simultaneously by both yield and stability. The non-parametric stability statistics in each the groups I, II, and III were positively and significantly correlated with each other, thus; any of these parameters could be considered as appropriate alternatives for each other. According to cluster analysis, soybean genotypes G6, G4, G8, G11, G9, G1, G7 and G2 were more stable varieties on the basis of mean seed yield and non-parametric stability statistics. In conclusion, both yield and stability should be considered simultaneously to exploit the useful effect of GEI and to make the selection of genotypes more precise and refined. Thus, the YSi, KR, TOP, RSM and δgy were more useful statistics in soybean breeding programmes and could be useful alternatives to parametric stability statistics. According to most non-parametric stability statistics, the genotypes G6 and G11 were more stable coupled with high seed yield; therefore, these genotypes might be used for genetic improvement of soybean and they must be released in studied regions and other regions in Egypt.

Comparison and association of parametric and non-parametric measures for identification of stable genotypes in finger millet

A study of phenotypic stability of 13 finger millet genotypes was conducted to assess genotype-environment interaction (GEI) and identify stable finger millet (Eleusine coracana (L.) Gaertn. subsp. coracana) genotypes for grain yield across four diverse locations in India. Both parametric and non-parametric stability statistics were used to identify stable finger millet genotypes. The parameters Wi2, σi2, Si(1), Si(2) identified similar stable genotypes, while different stable genotypes were identified by other measures. High correlation among non-parametric and parametric measures showed that these measures can be used alternatively. Only two stability measures, Ysi and YSI showed significant association with mean grain yield and Ysi was better choice for screening of genotypes for both yield and stability. The stable high yielding genotypes PPR 2773, VL 368, KOPN 942, VR 988, TNAU 1214 and GPU 45 can be deployed or included in breeding program for enhancing the finger millet productivity.

Assessment of partial resistance to rice blast disease

Assessment of resistance in any plant pathosystem involves the measurement, growth and development of the pathogen in the host under the influence of the environment. Partial resistance is generally a quantitative measure of disease progress over time and thus can be assessed quantitatively by means of (i) analysis of components of resistance, (ii) estimation of different parameters for evaluation of resistance, (iii) analysis of disease progress curves and (iv) analysis of stability of resistance. In this review, we discuss the analytical approaches on each of these four aspects, beginning with the components of resistance through the parameters to the multivariate analysis of disease progress curves involving principal component analysis, cluster analysis, factor analysis, pattern analysis and also the parametric and non-parametric stability statistics to arrive at the goal of selection of partial resistance. The merits and demerits of each method of assessment are discussed and the most effective method of assessment of partial resistance in rice blast pathosystem is suggested. The analysis of disease progress curves by multivariate analysis involving principal component analysis, cluster analysis, factor analysis, pattern analysis, AMMI analysis and estimation of stability values are recommended for determination of stable partial resistant genotypes.

Nonparametric phenotypic stability analysis in advanced barley (Hordeum vulgare L.) genotypes

The development of genotypes with adaptation to a wide range of environments is one of the most important goals of plant breeding programs. In order to compare nonparametric stability measures and to identify promising high-yield and stable barley (Hordeum vulgare L.), 20 barley genotypes selected from the Iran/ICARDA joint project and grown in nine environments during 2009-11 in Iran. Four nonparametric statistical tests of significance for genotype × environment (GE) interaction and 10 nonparametric measures of stability were used to identify stable genotypes in nine environments. Results of nonparametric tests of G×E interaction (Kubinger, Hildebrand, and Kroon/ Laan) and a combined ANOVA across environments, indicated the presence of both crossover and non-crossover interactions. Also, only TOP and rank-sum values were positively associated with high yield. Thus, in the simultaneous selection for high yield and stability, only the rank-sum and TOP methods were useful in terms of the principal component analysis results, and correlation analysis of nonparametric stability statistics and yield. According to these stability parameters (TOP and rank-sum), three genotypes (G13, G12, and G17) were the most stable for grain yield. The results also revealed that based on nonparametric test results, stability could be classified into three groups, according to agronomic and biological concepts of stability.

EVALUATION OF GENOTYPE BY ENVIRONMENT INTERACTION FOR GRAIN YIELD IN DURUM WHEAT USING NON-PARAMETRIC STABILITY STATISTICS

If genotype (G) ranks change from one environment (E) to another, genotype by environment interactions (GEI) reflects the need for testing Gs in numerous Es in order to obtain reliable results. The aim of this study was to compare 16 non-parametric stability statistics (NPSSs) for GEI on grain yields of 15 durum wheat genotypes, consisting of 11 advanced lines selected from Turkish National Durum Wheat Breeding Program (TNDWBP) and four checks, tested in 12 rain-fed environments during the 2 cropping seasons (2009–2010 and 2010-2011) in Turkey. The combined ANOVA indicated that G, E and GEI effects were significant for grain yield. According to analyses of NPSSs, the highest in ranking (TOP), percentage of adaptability (PA), rank mean (RM) and yield-stability (YS) statistics were positively associated with grain yield and therefore characterized under the dynamic concept of stability. In addition, spearman rank correlation analysis revealed that only TOP, PA, RM and YS statistics would be useful for simultaneous selection for high grain yield and stability. Based on the 16 NPSSs used in this study, Dumlupinar cultivar (G15) was both the most stable and one of the highest yielding ones. On the other hand, G5 and G7 were the most stable ones among the advanced lines tested, but their yield performances were lower. As a result, this study showed that the crossing block of TNDWBP should be enriched by germplasm being capable of dynamic stability, wide adaptation and higher yielding.

Graphic analysis of yield stability in new improved lentil (Lens culinaris Medik.) genotypes using nonparametric statistics

Yield stability is an interesting feature of today’s lentil breeding programs, due to the high annual variation in mean yield, particularly in the arid and semi-arid areas. The genetic effects including genetic main and genotype × environment (GE) interaction effects for grain yield of eighteen lentil (Lens culinaris Medik.) genotypes were studied with fourteen nonparametric stability statistics. Results of five distinct nonparametric tests of GE interaction and combined ANOVA showed there were both additive and crossover interaction types and genotypes varied significantly for grain yield. According to most of the nonparametric stability statistics, genotypes G5, G6, G8 and G18 were the most stable genotypes. Considering mean yield versus stability values via their plotting, indicates that genotypes G2, G11 and G14 following to G5, G16 and G18 were the most favorable genotypes. None of the nonparametric stability statistics were correlated with mean yield and so had static concept of stability. Our results confirmed that rankings of genotypes within environments and using mean yield information permit ease of interpretation of nonparametric results. Finally genotypes G2 (FLIP 92-12L), G11 (Gachsaran) and G14 (ILL 6206) were found to be the most stable and high mean yielding genotype and thus recommended for commercial release. Such an outcome could be used to delineate predictive, more rigorous recommendation strategies as well as to help define stability concepts for lentil and other crops.

Identification of the most stable genotypes in multi-environment trials by using nonparametric methods

Genotype performances in multi-environment trials are usually analyzed by different univariate and multivariate parametric models for assessing yield stability and genotype × environment (GE) interaction investigation. One of the alternative strategies can be nonparametric statistics approach which is particularly useful in situations where parametric statistics fail. For an estimation of yield stability of genotypes in various environments two new nonparametric stability statistics (NSi(1)and NSi(2)) have been used which are based upon the ranks of the genotypes in each environment. These statistics use median as a nonparametric central tendency, and two nonparametric index of statistical dispersion as interquartile range and inter-decile range. The NSi(1) and NSi(2) nonparametric stability statistics which presented here is similar to the nature and concept of environmental coefficient of variation. Results indicated that the most stable genotype based on the lowest values of these two nonparametric statistics, had the highest mean yield among studied genotypes. Plotting of mean yield versus NSi(1) and NSi(2) verified the above results and indicated that the highest mean yielding genotype is identified as the most stable genotype. These nonparametric statistics would be useful for simultaneous selection for mean yield and stability. They can be very helpful in selection for yield stability and determination of favorable genotypes in plant breeding programs.

Non-parametric stability analyses of dough properties in wheat

Literature has unveiled that a paper has not been published yet on using non-parametric stability statistics (NPSSs) for evaluating genotypic stability in dough properties of wheat. Accordingly, the effects of genotype (G), environment (E) and GE interaction (GEI) on alveograph parameters, i.e. dough baking strength (W) and its tenacity (P)/extensibility (L), of 18 wheat (T. aestivum L.) genotypes were studied under irrigated field conditions in an 8-year trial (2006-2014) in central Turkey. Furthermore, genotypic stability for W and P/L was determined using 8 NPSSs viz. RM-Rank mean, RSD-Rank’s standard deviation, RS-Rank Sum, TOP-Ranking, Si(1), Si(2), Si(3) and Si(6) rank statistics. The ANOVA revealed that W and P/L were primarily controlled by E, although G and GEI also had significant effects. Among the 8 NPSSs, only RM, RS and TOP statistics were suitable for detecting the genotypes with high stable and bread making quality (e.g. G1 and G17). In conclusion, using RM, RS and TOP statistics is advisable to select for dough quality in wheat under multi-environment trials (METs).