Comparing Relationships among Yield and Its Related Traits in Mycorrhizal and Nonmycorrhizal Inoculated Wheat Cultivars under Different Water Regimes Using Multivariate Statistics

Multivariate statistical techniques were used to compare the relationship between yield and its related traits under noninoculated and inoculated cultivars with mycorrhizal fungus (Glomus intraradices); each one consisted of three wheat cultivars and four water regimes. Results showed that, under inoculation conditions, spike weight per plant and total chlorophyll content of the flag leaf were the most important variables contributing to wheat grain yield variation, while, under noninoculated condition, in addition to two mentioned traits, grain weight per spike and leaf area were also important variables accounting for wheat grain yield variation. Therefore, spike weight per plant and chlorophyll content of flag leaf can be used as selection criteria in breeding programs for both inoculated and noninoculated wheat cultivars under different water regimes, and also grain weight per spike and leaf area can be considered for noninoculated condition. Furthermore, inoculation of wheat cultivars showed higher value in the most measured traits, and the results indicated that inoculation treatment could change the relationship among morphological traits of wheat cultivars under drought stress. Also, it seems that the results of stepwise regression as a selecting method together with principal component and factor analysis are stronger methods to be applied in breeding programs for screening important traits

Modeling and determining the best combination of nitrogen and irrigation levels for achieving high yield in sweet corn

In order to model the combination of nitrogen application and irrigation level on corn yield, a three-years experiment (2018, 2019, and 2020) was perfumed based on a split-plot design. Four levels of Nitrogen fertilizer (0, 75, 125, 175, and 225 kg ha−1) and three levels of irrigation (100%, 80%, and 60% FC) were applied in all three years. Different methods and models were tested to simulate the relationship between yield and nitrogen along with irrigation and the final results indicated that interaction term between irrigation regimes and nitrogen fertilizer levels is the most influential source on kernel yield of sweet corn. This is the first time considering a model that includes this interaction term in modeling the kernel yield of sweet corn. In addition, our results showed that a complete polynomial model would complex the explanation of the model and it can be replaced with an adjusted model in which irrigation, the square of nitrogen levels, and their interaction are the only sources with no significant loss of goodness of fit. The negative coefficient of squared nitrogen treatment indicated that under a lower irrigation level (around 60% FC) higher nitrogen fertilizer level than about 180 might lead to slightly decrease in the kernel yield. Overall, providing water of about 90% to 100% FC and a nitrogen level of about 180 kg ha−1 is recommended to reach a high kernel yield and higher economic efficiency.12 month embargo; published online: 28 April 2022This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Screening barley varieties tolerant to drought stress based on tolerant indices

With regards to the importance of drought stress and genotype screening under stress conditions, the current study was conducted to evaluate barley varieties in response to drought stress and find the tolerant ones, along with the determination of the influential ratio of each yield component on grain yield under both conditions. Accordingly, 25 barley varieties were evaluated under two water regimes including 100% (normal condition) and 50% (drought stress) of field capacity. Cluster analysis grouped the genotypes into four and three clusters under normal and stressful conditions, respectively, indicating that drought might limit the phenotypic variability among the verities. Based on the feature selection, spike weight, leaf area, and grain number per spike under normal condition, and the number of fertile spikelets and grain number per spike under water deficit conditions were the most influential traits on grain yield which verifies the impact of drought on the relationship among the agronomic traits. The overall results of biplot showed that varieties Danesiah, Eram, and Yoosef, which had higher grain yield than average for both conditions, were suitable varieties in order to be screened for both normal and drought stress conditions. Finally, according to the results of correlation of tolerance indices with grain yield, stress tolerance index (STI), harmonic mean productivity (HMP), and mean productivity (MP) which showed high correlations with grain yield under both conditions can be introduced as proper indices for screening tolerance genotypes of barley. © 2021 Taylor & Francis Group, LLC.12 month embargo; published online: 13 August 2021This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Improved strategy of screening tolerant genotypes in drought stress based on a new program in R-language: a practical triticale breeding program

In order to improve the efficiency of breeding programs related to abiotic stresses, a new package in R-based-language, “PBTolindex,” was introduced to distinguish tolerant genotypes to drought stress. Accordingly, a dataset of a practical breeding program on 30 triticale genotypes cultivated under drought stress and normal irrigation conditions in six different environments was evaluated. Correlation plot, scatter plot matrix, 3 D plot, and biplot along with indices’ values and their correlation coefficients were automatically produced as output files for considering the tested genotypes. Additionally, heatmap, a novel data mining method, was successfully applied for the first time in tolerance analysis. Our results indicated that no single suitable tolerance index could be suggested as the best one, and for any other study, different indices should be considered. Furthermore, the outputs of testing triticale genotypes indicated no suitable genotypes for both conditions. However, genotype ELTTCL15 for normal condition and genotypes ET-90-7, ELTTCL21, and ELTTCL18 for stress conditions were recommended by the program. Testing and identifying genotypes by heatmap and principal component analyses showed that the output of our program was accurate. Therefore, using this source-code in future plant breeding projects based on stress indices in any plant species is recommended.12 month embargo; published online: 13 July 2022This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]