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Soil properties can be influenced by slope location due to erosion, deposition, and mass movement. There are conflicting findings regarding the impact of the slope position on soil properties and elemental concentrations. This study aimed to examine the influence of slope position on the variability of soil properties and elemental concentrations along a hillslope landscape, using different multivariate approaches. This research was conducted in three distinct locations within the Cross River State, Nigeria: Ishibori in Ogoja, Agoi Ibami in Yakurr, and Mfamosing in Akamkpa. Three profile pits were dug on the crest, two on the middle slope, and three on the valley bottom, resulting in eight pits representing these locations. Fifty-three soil samples were collected from these pits and subjected to laboratory analyses. The lowest values were obtained for Bd, sand, and pH at all three slope positions. At the same time, the highest values were obtained for AP, SOC and Mn at the three different slope positions (crest, middle slope, and valley bottom). Soil properties, such as exchangeable acidity, bulk density, clay content, sand content, and soil organic carbon, were positively or negatively correlated (p < 0.05) with soil depth and elemental concentrations, depending on the location in the landscape. Exploring the interrelationship via a structural equation model (SEM), we observed that clay showed a strong positive correlation with sand and a moderate to weak negative correlation with sand and silt. The correlation coefficients between the studied slope positions were –0.88, –0.78, and –0.51, respectively. These results indicate that as the soil material at the crest decreased, there was a corresponding decrease in the materials at the middle slope and valley bottom. In the ANOVA, bulk density values in the crest and middle slope were comparable, but higher than those in the valley bottom soil. The potassium concentration was significantly different between the crest and middle slopes. Based on these findings, it can be concluded that topography significantly affects soil properties and that these differences should be considered when selecting and managing land for agricultural purposes. To improve soil fertility and productivity, it is recommended to implement proper soil management practices, such as nutrient management and erosion control, to reduce the impact of topographical differences on soil properties. [ABSTRACT FROM AUTHOR]

In this work, we tested different combinations of sampling strategies, random sampling and conditioned Latin Hypercube sampling (cLHS)] and sample ratios (10% = 147 and 25% = 368) to predict soil phosphorus and potassium contents, previously estimated using standard laboratory protocols. Other environmental covariates, used as input data for prediction, were obtained from different sources (multispectral Landsat-OLI 8 image, WorldClim database, ISRIC soil database, and ASTER-GDEM). Our findings showed that random sampling was suitable for predicting phosphorus, while the conditioned Latin Hypercube sampling was suitable for predicting potassium. Furthermore, we observed that when the sample ratio increased from 10 to 25%, model accuracy improved in random sampling and cLHS for phosphorus and potassium prediction. However, before generalizing these findings, we recommend that further studies be conducted under different conditions (climate, soil types and parent materials) and testing other sample ratios to determine the best sampling strategy with the optimum ratio to predict soil nutrients better. [ABSTRACT FROM AUTHOR]