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This study explores the suitability of different biomass feedstocks for biochar production and their effects on soil health and crop yield. Two planting seasons were conducted, involving cucumber as the test crop and eleven treatments combining biochar and poultry manure. Soil analysis revealed initial soil conditions with high sand content and low pH. Poultry manure and biochar exhibited pH, organic carbon, and nutrient level variations. Significant differences in cucumber growth and yield were observed, with the longest vine length in plots treated with palm kernel husk biochar and poultry manure. Residual effects in the second planting season displayed similar trends. Soil pH, organic carbon, and total nitrogen remained consistent between seasons, while available phosphorus increased significantly in plots amended with goat manure biochar and poultry manure. Calcium, magnesium, potassium, and sodium contents also varied. Fruit length, weight, and yield were significantly improved by biochar treatments, with the combination of palm kernel husk biochar and poultry manure yielding the highest fruit weight. Correlation and structural equation analyses (p < 0.05) highlighted the relationships between plant characteristics, soil properties, and fruit indices, emphasizing the importance of nitrogen and phosphorus in supporting fruit development. The study suggests that biochar application enhances soil nutrients, crop growth, and fruit yields while reducing reliance on chemical fertilizers. It recommends considering biochar for land reclamation and as an alternative to traditional fertilizers, supported by appropriate regulations. [ABSTRACT FROM AUTHOR]

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]

This study tested and evaluated a suite of nine individual base learners and seven model averaging techniques for predicting the spatial distribution of soil properties in central Iran. Based on the nested-cross validation approach, the results showed that the artificial neural network and Random Forest base learners were the most effective in predicting soil organic matter and electrical conductivity, respectively. However, all seven model averaging techniques performed better than the base learners. For example, the Granger–Ramanathan averaging approach resulted in the highest prediction accuracy for soil organic matter, while the Bayesian model averaging approach was most effective in predicting sand content. These results indicate that the model averaging approaches could improve the predictive accuracy for soil properties. The resulting maps, produced at a 30 m spatial resolution, can be used as valuable baseline information for managing environmental resources more effectively. [ABSTRACT FROM AUTHOR]