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In crop breeding programs, biplot analysis is a well-known statistical method. This study aimed to survey the genotype × environment interaction (GEI) on grass pea genotypes in Iran. The experiment was conducted in twelve environments (four separate sites: Gachsaran, Kuhdasht, Mehran, and Shirvanchardavol) over three sequential years (2017, 2018, and 2019) with sixteen grass pea genotypes. The purpose of this research was to utilize the GGE biplot as a tool to identify the superior genotypes of grass peas. The results for the combined analysis of variance, genotypes, and the GEI revealed a significant impact (p < 0.001) on forage yield. Moreover, genotype × environment interaction responded differently under various climatic conditions. The interaction components evaluated by the biplots revealed the genotypes' predominant effect and the significant genotype × environment interactions (GEI). The first two principal components (PCs) interpreted up to 93.11% of the total variation in the GGE model (PC1 = 53.30%, PC2 = 37.80%). GGE biplot analysis categorized the studied environments into two mega-groups for forage yield. Genotype G11 (Russia) was superior in terms of mean forage yield (5.362 t/ha). The genotypes that performed best in each environment, were genotypes G11 (Russia) and G8 (Bangladesh-I). Among these genotypes, G11 (Russia) was the highest-yielding genotype in the field. The Kohdasht site was the most discerning and representative test environment for crop yield. The selected genotypes are recommended for breeding programs aimed to improve forage yield in the tested sites or similar agroecological areas. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the genetic diversity of 140 barley advanced lines, using Selection Index of Ideal Genotype (SIIG) and Adjusted Selection Index of Ideal Genotype (ASIIG) indicators for introduction and cultivation of suitable lines in rainfed conditions. The highest coefficient of variation was related to the type of spike, flag leaf area, seeds per spike, peduncle length, flag leaf width, and early growth vigor. The stepwise regression showed that the early growth vigor, 1000-seeds weight, spike type, days to heading, plant height, number of seeds per spike, and flag leaf length were the most critical components, which affect the grain yield under the rainfed conditions. The results of SIIG showed that lines 4, 43, 47, 40, 70, 96, 137, 3, 57, and 45 were the best lines with the highest SIIG values. The SIIG index divided all lines into seven groups; the average grain yield of lines in groups I and II were higher than the average grain yield of control cultivars and the other groups. According to ASIIG plots, lines 47, 3, 43, 45, 137, 96, and 40 were placed in the ideal quarter. Finally, the SIIG and ASIIG were compatible in selecting the best lines. ASIIG was more suitable for separating the ideal lines, because it participated all traits commensurate with their importance in the end product quantity and quality. [ABSTRACT FROM AUTHOR]

A supervised feedforward artificial neural network (ANN) trained with backpropagation algorithms, along with multiple linear regression (MLR) model, was applied to predict the biological yield (BY) of grass pea. For this purpose, a four-year study (2008–2012) was carried out under rainfed conditions in three dryland stations in Iran, and a panel of 12 grass pea genotypes were evaluated for agronomic and phonologic characters. Stepwise regression (SWR) and principal component analysis (PCA) were employed to evaluate 15 input parameters. To discover the optimum ANN structure, different training algorithms, transfer functions, number of hidden layers and neuron in each layer were studied and optimized using Taguchi's method. The multilayer perceptron (MLP) model with tangent sigmoid (tagsig) transfer function, Levenberg–Marquardt learning algorithm, and two hidden layers was identified as the best model to predict the BY of the grass pea. The sensitivity analysis of inputs revealed that seed yield (SY) followed by thousand seed weight (TSW) and number of days to maturity (NDM), respectively, were the most influential factors in predicting BY in both models. According to the adjusted ANN model, early flowering genotypes with long maturity and high TSW should be considered as the best model for enhancing BY in breeding programs. Abbreviations: ANFIS: Adaptive neuro-fuzzy inference system; ANN: Artificial neural network; BC: Bayesian classification; BY: Biological yield; CL: Capsule length; CRBD: Randomized complete block design; CW: Capsule weight; GE: Genetic expression, FY: Forage yield; HI: Harvest index; MAE: Mean absolute error; MLR: Multi linear regression; MLP: Multilayer perceptron; NCP: Number of capsule per plant; NDF: Number of days to flowering; NDM: Number of days to maturity; NSC: Number of seed per capsule; NSC: Number of seed per capsule; NSP: Number of seed per plant; ODAP: β-N-oxalyl-L-α,β-diaminopropionic acid; OA: Orthogonal array; PA: Path analysis; PCA: Principal component analysis; PH: Plant height; RMSE: Root mean square error; SAS: Statistical analysis system; SFP: Seed filling period; SWR: Stepwise regression; SY: Seed yield; TOL: Tolerance; TSW: Thousand seed weight; VIF: Variance inflation factor; VIG: Vigority; WY: Wet yield. [ABSTRACT FROM AUTHOR]

This research was carried out to estimate yield stability of 18 barley breeding lines using different parametric and non-parametric statistics across 15 environments during 2012-2015 growing seasons. The results of combined analysis of variance (ANOVA) and additive main effect and multiplicative interaction (AMMI) analysis showed that environments (E), genotypes (G), GE interaction, as well as the first four interaction principal component axes were highly significant, suggesting that the lines interacted differentially with environments, so further general adaptability and stability analysis across environments should be followed before being introduced for cultivation. Based on correlation coefficient and principal components analysis (PCA), most of the non-parametric statistics were significantly inter-correlated with parametric statistics, hence seem to be suitable alternatives to complement parametric statistics. Furthermore, according to the static and dynamic concepts of stability, the results revealed that stability statistics can be clustered into three groups. The overall stability analysis following different stability methods concluded that four lines (G5, G7, G17 and G20) were highly stable for grain yield in rain-fed conditions of subtropical regions of Iran. Thus, these lines can be recognized as the most stable lines for cultivation in diverse environments of semi-warm regions of Iran. [ABSTRACT FROM AUTHOR]