Sustainable vegetative management plays a significant role in improving soil quality in degraded agricultural landscapes by enhancing soil microbial biomass. This study investigated the effects of grass buffers (GBs), biomass crops (BCs), grass waterways (GWWs), and agroforestry buffers (ABs) on soil microbial biomass and soil organic C (SOC) compared with continuous corn (Zea mays L.)–soybean [Glycine max (L.) Merr.] rotation (row crop [RC]) on claypan soils. The RC, AB, GB, GWW, and BC treatments were established in 1991, 1997, 1997, 1997, and 2012, respectively, and are located at Greenley Memorial Research Center in Missouri. Soil samples were collected in May 2018 from the 0‐ to

In this study, functional and numerical response tests, which are important components in the selection of biological control agent, were carried out. In functional response trials, the amount of food consumed, attack rate (a) and handling time (Th) were calculated for each developmental period, depending on the number of preys given after 24 hours. The obtained results were evaluated with the Holling. In numerical response experiments, the development of the predator insect was examined depending on the number of preys given in certain numbers (5, 10, 20, 40 and 80) and the data were recorded. This phase of the trials continued until the individuals died. At this stage of the trials, the reproductive response of the p

The aim of our study was to investigate the antiviral activity of the Corchorus olitorius family Tiliaceae cultivated in Iraq against measles virus, and to demonstrate an overview about chemical constituents and pharmacological activity of Corchorus olitorius L.

About150 gm Leaves of Corchorus. olitorius were defatted by maceration in hexane for 24 hrs. The defatted plant materials were subjected for extraction after filtration using Soxhlet apparatus, with aqueous methanol 85% as a solvent extraction for 24 hours, the extract was filtered, and the solvent was evaporated under reduced pressure using a rotary evaporator to get a dry extract of about 12 gm. About 4 gm from the residue was suspended in 100

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