Search

The aim of this study was to determine the effects of single and combined applications of plant-growth-promoting rhizobacteria (PGPR) bacteria on plant nutrition, biochemical content and fruit characteristics in Albion and Monterey strawberry cultivars. Bacillus subtilis OSU-142, Bacillus megaterium M3 and Paenibacillus polymyx were the PGPR used in the experiment. For each bacterial treatment, 10 mL of a 108 CFU mL−1 suspension was applied to the soil where Albion and Monterey cultivars were grown. PGPR bacteria were applied as single treatments and a mixture of equal amounts of these three bacterial species was applied as a mixed treatment. This study was carried out with a total of four different bacterial treatments and one control group. The highest fruit weight was obtained in the Monterey cultivar with 12.67 g in the Mix treatment and in the Albion cultivar with 11.79 g in the Bacillus megaterium M3 treatment. Regarding biochemical properties, Paenibacillus polymyxa was effective in influencing nutrient element content in fruits, while Bacillus subtilis OSU-142, Paenibacillus polymyxa and Bacillus megaterium M3 applications were more effective in leaf nutrient element content. It has been observed that the Mix treatment resulting from the combined use of bacteria, rather than their separate use, has a greater impact on fruit weight. Consequently, it has been understood that PGPR bacteria are potentially effective in improving the agronomic, pomological, and biochemical characteristics of strawberry cultivars and can be used in studies and breeding programs aimed at increasing strawberry yield and quality. [ABSTRACT FROM AUTHOR]

The combination of high or low temperatures and high salt may cause significant harm to the yield, quality, and overall productivity of forage pea crops. The germination process, a crucial phase in the life cycle of forage peas, may be greatly influenced by varying temperature and salinity conditions. To comprehend the influence of these elements on the germination of forage peas, one must use many tactics, including the choice of resilient forage pea cultivars. The experiment aimed to evaluate the response of four forage pea cultivars (Arda, Ozkaynak, Taskent, and Tore) caused by various temperature (10 °C, 15 °C, and 20 °C) and salt (0, 5, 10, 15, and 20 dS m−1) conditions at the germination stage using multivariate analysis and machine learning methods. An observation of statistical significance (p < 0.01) was made regarding the variations between genotypes, temperature–salt levels, and the interaction of the observed factors: germination percentage (GP), shoot length (SL), root length (RL), fresh weight (FW), and dry weight (DW). The cultivar Tore had the best values for SL (1.63 cm), RL (5.38 cm), FW (1.10 g), and DW (0.13 g) among all the cultivars. On the other hand, the Ozkaynak cultivar had the highest value for GP (89.13%). The values of all of the parameters that were investigated decreased as the salt level rose, whereas the values increased when the temperature level increased. As a result, the Tore cultivar exhibited the highest values for shoot length, root length, fresh weight, and dry weight variables when exposed to a maximum temperature of 20 °C and a saline level of 0 dS m−1. It was determined that temperature treatment of fodder peas can reduce salt stress if kept at optimum levels. The effects of temperature and salt treatments on the germination data of several fodder pea cultivars were analyzed and predicted. Three distinct machine learning algorithms were used to create predictions. Based on R2 (0.899), MSE (5.344), MAPE (6.953), and MAD (4.125) measures, the MARS model predicted germination power (GP) better. The GPC model performed better in predicting shoot length (R2 = 0.922, MSE = 0.602, MAPE = 11.850, and MAD = 0.326) and root length (R2 = 0.900, MSE = 0.719, MAPE = 12.673, and MAD = 0.554), whereas the Xgboost model performed better in estimating fresh weight (R2 = 0.966, MSE = 0.130, MAPE = 11.635, and MAD = 0.090) and dry weight (R2 = 0.895, MSE = 0.021, MAPE = 12.395, and MAD = 0.013). The results of the research show that the techniques and analyses used can estimate stress tolerance, susceptibility levels, and other plant parameters, making it a cost-effective and reliable way to quickly and accurately study forage peas and related species. [ABSTRACT FROM AUTHOR]

Industrial hemp (Cannabis sativa L.) is a highly recalcitrant plant under in vitro conditions that can be overcome by employing external stimuli. Hemp seeds were primed with 2.0–3.0% hydrogen peroxide (H2O2) followed by culture under different Light Emitting Diodes (LEDs) sources. Priming seeds with 2.0% yielded relatively high germination rate, growth, and other biochemical and enzymatic activities. The LED lights exerted a variable impact on Cannabis germination and enzymatic activities. Similarly, variable responses were observed for H2O2 × Blue-LEDs combination. The results were also analyzed by multiple regression analysis, followed by an investigation of the impact of both factors by Pareto chart and normal plots. The results were optimized by contour and surface plots for all parameters. Response surface optimizer optimized 2.0% H2O2 × 918 LUX LEDs for maximum scores of all output parameters. The results were predicted by employing Multilayer Perceptron (MLP), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) algorithms. Moreover, the validity of these models was assessed by using six different performance metrics. MLP performed better than RF and XGBoost models, considering all six-performance metrics. Despite the differences in scores, the performance indicators for all examined models were quite close to each other. It can easily be concluded that all three models are capable of predicting and validating data for cannabis seeds primed with H2O2 and grown under different LED lights.Key message: Chemical priming of H2O2 with LED lights regulates the cannabis plant growth. Use of Pareto chart and normal plots to rank the input factor, their impact, and efficiency in percentage. Use of optimizing tools like contour plots, surface plots, and response optimizers to optimize H2O2 and LED lights for cannabis. Data validation and prediction using AI/ML-based MLP, RF, and XGBoost models. [ABSTRACT FROM AUTHOR]

The genus vetch (Vicia L.) is grown worldwide for fodder, hay, grain and silage, and rich in protein, mineral substances, vitamins and an essential source of roughage in animal husbandry. However, genetic characterization studies in vetch are minimal. In this study, the genetic characterization of a total 37 accessions of five vetches (Vicia L.) species was investigated using SSR markers. A total of 18 SSR markers were used, and eight of them were showed polymorphism and used for genetic analysis of vetch accessions. The total number of alleles was 35, and the average number of alleles for each locus was determined as 4.38. The average heterozygote rate was found to be 0.49. The polymorphism information content (PIC) value varied between 0.23 and 0.77, and the average value was 0.44. Although almost a clear distinction was observed among the species, very high similarities were found between some cultivars within the same species. This similarity may be due to the narrow structure of the vetch genome or the inability of the SSR markers used in this study to distinguish the narrow structure of the vetch genome. The results reported here will be contributed to future germplasm management efforts and for comparative studies in vetch. [ABSTRACT FROM AUTHOR]