Association between Grain Size and Shape and Quality Traits, and Path Analysis of Thousand Grain Weight in Iranian Bread Wheat Landraces from Different Geographic Regions

Grain characteristics, particularly grain weight, grain morphology, and grain protein content (GPC), are important components of grain yield and quality in wheat. A set of 98 bread wheat landraces from different geographic regions of Iran were used across 2013-2014 and 2014-2015 to determine the phenotypic diversity and relations between thousand grain weight (TGW), grain morphology and grain quality. A high-throughput method was used to capture grain size and shape. The genotypes were significantly different (P < 0.001) for all traits which reflects the high levels of diversity. A moderate to high broad sense heritability was found for all traits and ranged between 0.68 and 0.95 for grain yield and factor from density (FFD), respectively. Significant positive correlations were observed between TGW and grain size (or shape) exception of aspect ratio (AR) and roundness. However, grain quality traits, especially GPC had significant negative correlation with TGW. Based on stepwise regression analysis by taking TGW as dependent variable, grain volume, FFD, width, perimeter and Hardness Index (HI) were recognized as the most important traits and explained more than 99.3% of total variation of TGW. The path analysis revealed that FFD has maximum direct effect on TGW followed by volume, whereas perimeter and width had relatively less direct effect on TGW. According to cluster analysis, landraces separated into 5 clusters, and cluster III and IV had the maximum and minimum average for the most traits, respectively. Our study provides new knowledge on the relations between TGW, grain morphology and grain quality in bread wheat, which may aid the improvement of wheat grain weight trait in further research

An efficient estimation of crop performance in sheep fescue (Festuca ovina L.) using artificial neural network and regression models

Abstract Festuca ovina L. (sheep fescue), a perennial grass plant found in mountainous regions, is important from both an ecological and economic viewpoint. However, the variability of biological yield of sheep fescue due to its reliance on different characteristics makes it difficult to accurately prediction using classic modeling techniques. In this study, machine learning methods and multiple regression models (linear and non-linear) are used to investigate the interdependence of various morphological and physiological characteristics on accurate prediction of the biological yield (BY) of sheep fescue. Principal components analysis and stepwise regression were used to select six agronomic parameters i.e. thousand seed weight (TSW), relative water content (RWC), canopy cover (CC), leaf area index, number of florescence, and viability (VA), while the output variable was BY. To optimized the artificial neural network (ANN) structure, different transfer functions and training algorithms, different number of neurons in each layer, different number of hidden layers and training iteration were tested. The accuracy of the models and algorithms is analyzed by root mean square error (RMSE), mean absolute error (MAE), and determination coefficient (R2). According to the findings, ANN models were more accurate than regression models. The ANN model with two hidden layers (i.e. structure of 6–4–8–1) which had RMSE, MAE and R2 scores of 0.087, 0.065 and 0.96, respectively, was discovered as the best model for predicting the BY. In addition, result of the sensitivity analysis showed TSW, RWC and CC, in that order, were the variables most important for high-quality BY estimation in both models regardless of input combination. Finally, the paper concludes that early flowering sheep fescue genotypes with long maturation and great TSW must be regarded as the most suitable model for increasing BY in breeding projects

Association between Grain Size and Shape and Quality Traits, and Path Analysis of Thousand Grain Weight in Iranian Bread Wheat Landraces from Different Geographic Regions

Grain characteristics, particularly grain weight, grain morphology, and grain protein content (GPC), are important components of grain yield and quality in wheat. A set of 98 bread wheat landraces from different geographic regions of Iran were used across 2013-2014 and 2014-2015 to determine the phenotypic diversity and relations between thousand grain weight (TGW), grain morphology and grain quality. A high-throughput method was used to capture grain size and shape. The genotypes were significantly different (P < 0.001) for all traits which reflects the high levels of diversity. A moderate to high broad sense heritability was found for all traits and ranged between 0.68 and 0.95 for grain yield and factor from density (FFD), respectively. Significant positive correlations were observed between TGW and grain size (or shape) exception of aspect ratio (AR) and roundness. However, grain quality traits, especially GPC had significant negative correlation with TGW. Based on stepwise regression analysis by taking TGW as dependent variable, grain volume, FFD, width, perimeter and Hardness Index (HI) were recognized as the most important traits and explained more than 99.3% of total variation of TGW. The path analysis revealed that FFD has maximum direct effect on TGW followed by volume, whereas perimeter and width had relatively less direct effect on TGW. According to cluster analysis, landraces separated into 5 clusters, and cluster III and IV had the maximum and minimum average for the most traits, respectively. Our study provides new knowledge on the relations between TGW, grain morphology and grain quality in bread wheat, which may aid the improvement of wheat grain weight trait in further research

Agrobacterium rhizogenes transformed soybeans with AtPAP18 gene show enhanced phosphorus uptake and biomass production

Low-phosphorus stress is a challenging factor in limiting plant development. Soybean is cultivated in soils often low in phosphorus. However, on average 65% of total P is in the form of organic phosphates, which are unavailable to plants unless hydrolyzed to release inorganic phosphate. One approach for enhancing crop P acquisition from organic P sources is boosting the activity of acid phosphatases (APases). This study seeks to understand the role of an Arabidopsis (Arabidopsis thaliana) purple APase gene (AtPAP18) in soybean. Thus, the gene was isolated and a final vector (AtPAP18/pK7GWG2D) was built. Composite soybean plants were created using Agrobacterium rhizogenes-mediated transformation. A. rhizogenes K599 carrying the AtPAP18/pK7GWG2D vector with egfp as a reporter gene was used for soybean hairy root transformation. Analysis of Egfp expression detected fluorescence signals in transgenic roots, whereas there was no detectable fluorescence in control hairy roots. The enzyme assay showed that the APase activity increased by 2-fold in transgenic hairy roots. The transformed hairy roots displayed an increase in plant soluble P and total P contents, as compared with the control plants, leading to improved biomass production. RT-PCR analysis revealed high expression levels of AtPAP18 in transformed hairy roots. It is noteworthy that these primers amplified no PAP18 transcript in control hairy roots. Taken together, the findings demonstrated that overexpression of the AtPAP18 gene offers an operative tactic to reduce the utilization of inorganic phosphorus (Pi) fertilizer through increased acquisition of soil Pi, especially improving the crop yield on soils low in available P