Spatial Variability of Soil Properties around Baturiya Sanctuary, Jigawa State, Nigeria

Soil properties intricately vary spatially owing to several natural and anthropogenic factors including parent material, terrain as well as land use. The aim of this study is to assess the spatial variability of soil samples collected from three different land use types namely: reserved area, parkland and farmland around Baturiya Sanctuary, northwestern Nigeria with a view to providing information that will assist the government in planning and conservation of the area. Free traverse sampling technique was used to collect soil samples at the depth of 0-30cm. Laboratory analysis was done for the following parameters: bulk density, PSD, phosphorous, pH, EC, total nitrogen, exchangeable bases (Mg, Na, and K), and CEC. Geostatistical technique (semivariogram analysis) was used to test variation in soil properties. Result of the study depicted that It also indicated that BD (1.24 g/cm3), clay (22%), total nitrogen (0.25 g/kg), available phosphorous (32.61 mg/g), OC (1.6%) and Mg (0.05) are highest in reserved area. Also sand (55%) and silt (29%), pH (5.0), EC (522), Na (0.007), K (0.44) and CEC (4.5meq/100g) are highest in farmland. The variogram based nugget-sill ratio showed strong dependency with 0 (N, EC, OC) and weak dependency 1 (BD, Na) on the scale of 0.25 high, 0.25 – 0.75 moderate and 0.75 weak. In conclusion, this study found that soil properties in area showed high to moderate spatial dependency except for BD, Mg, K, and Na which showed low spatial autocorrelation owing increasing human activities in the area. This study depicted that apparently limitation by few samples have influenced the pattern in the result otherwise spatial variability of certain elements may be more discernible and beyond reasons such land use and parent materials

Regularization Effects in Deep Learning Architecture

This research examines the impact of three widely utilized regularization approaches -- data augmentation, weight decay, and dropout --on mitigating overfitting, as well as various amalgamations of these methods. Employing a Convolutional Neural Network (CNN), the study assesses the performance of these strategies using two distinct datasets: a flower dataset and the CIFAR-10 dataset. The findings reveal that dropout outperforms weight decay and augmentation on both datasets. Additionally, a hybrid of dropout and augmentation surpasses other method combinations in effectiveness. Significantly, integrating weight decay with dropout and augmentation yields the best performance among all tested method blends. Analyses were conducted in relation to dataset size and convergence time (measured in epochs). Dropout consistently showed superior performance across all dataset sizes, while the combination of dropout and augmentation was the most effective across all sizes, and the triad of weight decay, dropout, and augmentation excelled over other combinations. The epoch-based analysis indicated that the effectiveness of certain techniques scaled with dataset size, with varying results