To map the genomic regions and control chlorophyll fluorescence attributes under normal, salinity-, and drought-stress conditions in barley (Hordeum vulgare L.) at the seedling stage, an experiment was conducted in 2019–2020 using 106 F8 lines resulting from the cross between Badia × Kavir. Initially, the different chlorophyll fluorescence parameters were evaluated. Under drought stress, the highest decrease was related to REo/CSm (59.56%), and the highest increase was related to dV/dto (77.17%). Also, under salinity stress, the highest decrease was related to Fv/Fo (59.56%), and the highest increase was related to DIo/RC (77.17%). Linkage maps were prepared using 152 SSR polymorphic markers, 72 ISSR alleles, 7 IRAP alleles, 29 CAAT alleles, 27 Scot alleles, and 15 iPBS alleles. The obtained map accounted for 999.2 centi-Morgans (cM) of the barley genome length (92% of the whole barley genome). The results indicated the importance of chromosomes 3, 2, and 7 in controlling ABS/CSm, Area, ETo/CSm, Fm, Fv, and ETo/RC under drought stress. qEToRCD-7, as a major QTL, controlled 18.3% of ETo/RC phenotypic variation under drought stress. Under salinity stress, the regions of chromosomes 2 and 7 (102 cM and 126 cM) controlled the parameters ABS/CSo, Fm, Fo, Fv, TRo/SCo, Area, ETo/CSm, and ETo/CSo. The results showed that chlorophyll fluorescence is an important parameter in the study of drought and salinity effects on barley. This is the first report of the investigation of changes in the genetic structure of quantitative genes controlling the fluorescence parameters associated with barley response to drought and salinity stresses in the Iranian barley RILs population. According to the obtained results, it is possible to use HVPLASC1B and EBmac0713 in normal conditions, ISSR21-2 and ISSR30-4 in drought conditions, and Bmac0047, Scot5-B, CAAT6-C, and ISSR30iPBS2076-4 in saline stress conditions to select genotypes with higher photosynthetic capacity in marker-assisted selection programs.

To identify the genomic regions for the physiological and morphological traits of barley genotypes under normal salinity and drought, a set of 103 recombinant inbred line (RIL) populations, developed between Badia and Kavir crosses, was evaluated under phytotron conditions in a completely randomized design in 2019. Linkage maps were prepared using 152 SSR markers, 72 ISSR, 7 IRAP, 29 CAAT, 27 SCoT, and 15 iPBS alleles. The markers were assigned to seven barley chromosomes and covered 999.29 centimorgans (cM) of the barley genome. In addition, composite interval mapping showed 8, 9, and 26 quantitative trait loci (QTLs) under normal, drought, and salinity stress conditions, respectively. Our results indicate the importance of chromosomes 1, 4, 5, and 7 in salinity stress. These regions were involved in genes controlling stomata length (LR), leaf number (LN), leaf weight (LW), and genetic score (SCR). Three major stable pleiotropic QTLs (i.e., qSCS-1, qRLS-1, and qLNN-1) were associated with SCR, root length (RL), and root number (RN) in both treatments (i.e., normal and salinity), and two major stable pleiotropic QTLs (i.e., qSNN-3 and qLWS-3) associated with the stomata number (SN) and LW appeared to be promising for marker-assisted selection (MAS). Two major-effect QTLs (i.e., SCot8-B-CAAT5-D and HVM54-Bmag0571) on chromosomes 1 and 2 were characterized for their positive allele effect, which can be used to develop barley varieties concerning drought conditions. The new alleles (i.e., qLWS-4a, qSLS-4, qLNS-7b, qSCS-7, and qLNS-7a) identified in this study are useful in pyramiding elite alleles for molecular breeding and marker assisted selection for improving salinity tolerance in barley.

Background Seed yield is controlled by additive and non-additive effects of genes, so predicting seed yield is one of the most important goals of rapeseed breeding in agricultural research. However, there is less information about the yield estimation of canola using neural network. In this research, three models of Multi-Layer Perceptron (MLP) neural network, Radial Basis Function (RBF) neural network and Support Vector Machine (SVM) were used to predict rapeseed yield. Network training was performed using phenological, morphological, yield and yield components, as well as data obtained from molecular markers of 8 genotypes and 56 hybrids. Results The obtained from the comparison of the efficiency of the models showed that the MLP model was able to predict the hybrid yield with the RMSE, MAE and R² equal to 226, 183 and 92% and the use of phenotypic data as model inputs in direct crosses with the highest accuracy. In the genetic evaluation section, according to the indicators obtained, it was found that molecular study is a powerful tool that can provide valuable information to the breeder. The results showed that among the 40 primers investigated, the ISJ10 primer had more resolving power than the other primers. Conclusions The use of molecular and phenotypic data as input data in the model showed that the MLP model had a lower error value in terms of RMSE and MAE and a higher R² than direct crosses in predicting the performance of reciprocal crosses. The proposed neural network model makes it possible to estimate the performance of each of the hybrids of the parents studied before crossing, which helps the breeder to focus on the best possible hybrids.

Extended Abstract Background: Black seed (Nigella sativa L.) from the Ranunculaceae family is one of the natural useful antioxidants, and the oil prepared from its seeds has various medicinal properties, including anti-cancer, anti-microbial, anti-hypertensive, anti-diabetic and increased immunity has been reported. Nigella sativa, like other plants, is constantly exposed to ...