A laboratory experiment was carried out at the College of Agriculture University of Baghdad in 2017. The aim was to improve the anatomical and physiological traits of broad bean seedling under salt stress by soaking it in salicylic acid. The concentrations of salicylic acid were 0, 10, and 20 mg L-1 and the electrical conductivity levels were 0, 3, and 6 dS m-1. The complete randomized design was used with four replications. The increasing of salicylic acid concentration up to 10 mg L-1 led to increasing the stem cortex thickness, stem vascular bundles thickness, and root cortex thickness significantly by (34.9,36.7,and 55 µm) respectively, while the treatment of 20 mg L-1 led to decreasing these traits by (28.2, 27.8, and 48.1 µm

A laboratory experiment was carried out at the College of Agriculture University of Baghdad in 2017. The aim was to improve the anatomical and physiological traits of broad bean seedling under salt stress by soaking it in salicylic acid. The concentrations of salicylic acid were 0, 10, and 20 mg L-1 and the electrical conductivity levels were 0, 3, and 6 dS m-1. The complete randomized design was used with four replications. The increasing of salicylic acid concentration up to 10 mg L-1 led to increasing the stem cortex thickness, stem vascular bundles thickness, and root cortex thickness significantly by (34.9,36.7,and 55 μm) respectively, while the treatment of 20 mg L-1 led to decreasing these traits by (28.2, 27.8, and 48.1 μm), compa

Accurate prediction and optimization of morphological traits in Roselle are essential for enhancing crop productivity and adaptability to diverse environments. In the present study, a machine learning framework was developed using Random Forest and Multi-layer Perceptron algorithms to model and predict key morphological traits, branch number, growth period, boll number, and seed number per plant, based on genotype and planting date. The dataset was generated from a field experiment involving ten Roselle genotypes and five planting dates. Both RF and MLP exhibited robust predictive capabilities; however, RF (R² = 0.84) demonstrated superior performance compared to MLP (R² = 0.80), underscoring its efficacy in capturing the nonlinear genoty

This study focuses on the biodegradation of oxymatrine insecticide by some soil fungi isolated from four agriculture stations. The results showed that the highest degradation rate 94.66% was recorded by Ulocladium sp. at 10 days and A. niger recorded the lowest degradation rate 45.86%, while at 20 days Ulocladium sp. also showed the highest degradation rate 94.98% and the lowest degradation rate reached to 82.49% with A.niger. The mix (Exerohilum sp.+Ulocladium sp.) recorded the highest degradation rate of oxymatrine insecticide 90.22%, 88.51%, 85.34%  at 4, 8 and 12 ppm.The use of mixed isolates enhanced the biodegradation process. There is no study of oxymatrine biodegradation