Rank correlation among different statistical models in ranking of winter wheat genotypes'

Several statistical methods have been developed for analyzing genotype×environment(GE)interactions in crop breeding programs to identify genotypes with high yield and stability performances.Four statistical methods,including joint regression analysis(JRA),additive mean effects and multiplicative interaction(AMMI)analysis,genotype plus GE interaction(GGE)biplot analysis,and yield–stability(YSi)statistic were used to evaluate GE interaction in20 winter wheat genotypes grown in 24 environments in Iran.The main objective was to evaluate the rank correlations among the four statistical methods in genotype rankings for yield,stability and yield–stability.Three kinds of genotypic ranks(yield ranks,stability ranks,and yield–stability ranks)were determined with each method.The results indicated the presence of GE interaction,suggesting the need for stability analysis.With respect to yield,the genotype rankings by the GGE biplot and AMMI analysis were significantly correlated(P<0.01).For stability ranking,the rank correlations ranged from 0.53(GGE–YSi;P<0.05)to0.97(JRA–YSi;P<0.01).AMMI distance(AMMID)was highly correlated(P<0.01)with variance of regression deviation(S2di)in JRA(r=0.83)and Shukla stability variance(σ2)in YSi(r=0.86),indicating that these stability indices can be used interchangeably.No correlation was found between yield ranks and stability ranks(AMMID,S2di,σ2,and GGE stability index),indicating that they measure static stability and accordingly could be used if selection is based primarily on stability.For yield–stability,rank correlation coefficients among the statistical methods varied from 0.64(JRA–YSi;P<0.01)to 0.89(AMMI–YSi;P<0.01),indicating that AMMI and YSi were closely associated in the genotype ranking for integrating yield with stability performance.Based on the results,it can be concluded that YSi was closely correlated with(i)JRA in ranking genotypes for stability and(ii)AMMI for integrating yield and stability.

Interpreting genotype × environment interactions for grain yield of rainfed durum wheat in Iran

Clustering genotype × environment(GE) interactions and understanding the causes of GE interactions are among the most important tasks in crop breeding programs. Pattern analysis(cluster and ordination techniques) was applied to analyze GE interactions for grain yield of 24 durum wheat(Triticum turgidum L. var. durum) genotypes(breeding lines and old and new cultivars) along with a popular bread wheat(Triticum aestivum) cultivar grown in 21 different rainfed environments during the 2010–2013 cropping seasons. To investigate the causes of GE interaction, several genotypic and environmental covariables were used. In a combined ANOVA, environment was the predominant source of variation,accounting for 81.2% of the total sum of squares(TSS), and the remaining TSS due to the GE interaction effect was almost seven times that of the genetic effect. Cluster analysis separated the environments into four groups with similar discriminating ability among genotypes, and genotypes into five groups with similar patterns in yield performance.Pattern analysis confirmed two major environmental clusters(cold and warm), and allowed the discrimination and characterization of genotype adaptation. Within the cold-environment cluster, several subclusters were identified. The breeding lines were most adapted to moderate and warm environments, whereas the old varieties were adapted to cold environments. The results indicated that winter rainfall and plant height were among the environmental and genotypic covariables, respectively, that contributed most to GE interaction for grain yield in rainfed durum wheat.

Field evaluation of durum wheat landraces for prevailing abiotic and biotic stresses in highland rainfed regions of Iran

Biotic and abiotic stresses are major limiting factors for high crop productivity worldwide. A landrace collection consisting of 380 durum wheat(Triticum turgidum L. var. durum) entries originating in several countries along with four check varieties were evaluated for biotic stresses:yellow rust(Puccinia striiformis Westendorf f. sp. tritici) and wheat stem sawfly(WSS) Cephus cinctus Norton(Hymenoptera: Cephidae), and abiotic stresses: cold and drought. The main objectives were to(i) quantify phenotypic diversity and identify variation in the durum wheat landraces for the different stresses and(ii) characterize the agronomic profiles of landraces in reaction to the stresses. Significant changes in reactions of landraces to stresses were observed.Landraces resistant to each stress were identified and agronomically characterized.Percentage reduction due to the stresses varied from 11.4%(yellow rust) to 21.6%(cold stress) for 1000-kernel weight(TKW) and from 19.9(yellow rust) to 91.9%(cold stress) for grain yield. Landraces from Asia and Europe showed enhanced genetic potential for both grain yield and cold tolerance under highland rainfed conditions of Iran. The findings showed that TKW and yield productivity could be used to assess the response of durum wheat landraces to different stresses. In conclusion, landraces showed high levels of resistance to both biotic and abiotic stresses, and selected landraces can serve in durum wheat breeding for adaptation to cold and drought-prone environments.

Evaluating spectral indices for determining conservation and conventional tillage systems in a vetch-wheat rotation

Conservation tillage(CT)systems,which consist of reduced and no-tillage systems,retain considerable quantities of crop residues on the soil surface.These crop residues perform as a barrier to wind and water to decrease soil erosion and evaporation.The use of remote sensing technology provides fast,objective and effective tool for estimating and measuring any agricultural event.The challenge is to differentiate the tillage systems by the crop residue cover on the soil surface.Spectrally derived normalized difference tillage index(NDTI),Shortwave infrared normalized difference residue index(SINDRI),cellulose absorption index(CAI)and Lignin-cellulose absorption index(LCA)were examined to distinguish their value as remote sensing methods for identifying crop residue cover in conventional and conservation tillage systems.Tillage treatments included conventional tillage(MD:Mouldboard plow+Disk harrow),reduced tillage(CD:Chisel plowþDisk harrow),minimum till(MT:Stubble cultivator),and no-tillage(NT_(1) and NT_(2):with standing stubble and standing stubble plus threshing residue,respectively).CAI had a linear relationship with crop residue cover,which the comparative intensity of cellulose and lignin absorption features near 2100 nm can be measure by it.Coefficients of determination(r^(2))for crop residue cover as a function of CAI and LCA were 0.89 and 0.79 respectively.Absorption specifications near 2.1 and 2.3 mm in the reflectance spectra of crop residues in minimum and no-tillage systems were related to cellulose and lignin.These specifications were not evident in the spectra of conventional tillage system.In this study the best index to use was CAI,which showed complete separation tillage systems,followed by LCA and NDTI.Four tillage intensity classes,corresponding to intensive(<6%residue cover),reduced(10–20%cover)minimum(25–40%)and no-tillage(>60%cover)tillage,were recognized in this study.