Soil C and N turnover rates and contents are strongly influenced by climates (e.g., mean annual temperature MAT, and mean annual precipitation MAP) as well as human activities. However, the effects of converting natural forests to intensively human-managed plantations on soil carbon (C), nitrogen (N) dynamics across various climatic zones are not well known. In this study, we evaluated C, N pool and natural abundances ofδ¹³C andδ¹⁵N in forest floor layer and 1-meter depth mineral soils under natural forests (NF) and plantation forest (PF) at six sites in eastern China. Our results showed that forest floor had higher C contents and lower N contents in PF compared to NF, resulting in high forest floor C/N ratios and a decrease in the quality of organic materials in forest floor under plantations. In general, soil C, N contents and their isotope changed significantly in the forest floor and mineral soil after land use change (LUC). Soilδ¹³C was significantly enriched in forest floor after LUC while bothδ¹³C andδ¹⁵N values were enriched in mineral soils. Linear and non-linear regressions were observed for MAP and MAT in soil C/N ratios and soilδ¹³C, in their changes with NF conversion to PF while soilδ¹⁵N values were positively correlated with MAT. Our findings implied that LUC alters soil C turnover and contents and MAP drive soilδ¹³C dynamic.

Forest plantation, either through afforestation or reforestation, has been suggested to reverse and mitigate the process of deforestation. However, uncertainties remain in the potential of plantation forest (PF) to sequestrate carbon (C) and nitrogen (N) compared to natural forest (NF). Soil C and N stocks require a critical and updated look at what is happening especially in the context of increasing rate of land use change and climate change. The current study was conducted in China’s Eastern forest to estimate soil C and N stocks in six depth layers (0–10, 10–20, 20–40, 40–60, 60–80 and 80–100 cm) and two forest types (NF and PF) at four sites along climate factors gradient. The results showed that the overall mean soil C and N amounts to a depth of 20 cm ranged from 2.6 ± 1.1 Mg ha⁻¹ to 38.6 ± 23.1 Mg ha⁻¹, and soil nitrogen stock ranged from 0.2 ± 0.1 Mg ha⁻¹ to 3.3 ± 1.5 Mg ha⁻¹. Moreover, a loss of C stock was observed at Qingyuan (QY) by −7%, Dinghushan (DH) by −26%, Jianfengling (JF) by −13% while that of N stock was observed at QY (−8%), DH (−19%) and JF (−12%) at both depth layers. These results indicate that NFs have a better capacity to accumulate soil C and N. The soil C and N decreased from the southeast to the northeast and increased from tropical to temperate mixed forests zone in the eastern part of the study area. The C and N stock mainly occurred in the topsoil and decreased significantly with depth. Moreover, soil C and N stocks increased with age of plantation. This study provides an overview of the current spatial distribution and soil stocks of C and N, as well as the effects of environmental factors on soil C and N stocks. It also indicated that, although mean annual temperature and mean annual precipitation are the key factors affecting the variations in soil C and N, their vertical and horizontal distribution differed in various aspects.

Acidic soils (pH < 5.5) cover roughly 30% of Sub-Saharan Africa. Low nitrogen fertilizer application (15 kg N ha⁻¹ yr⁻¹) has no effect on soil acidification in Sub-Saharan Africa (SSA). However, the effect of optimized fertilizer use on soil acidification (H⁺) in SSA crops remains unknown. This study intended to predict the spatial variation of H⁺ caused by optimized fertilizer use using data from 5782 field trials in SSA cropland. We used ensemble machine learning to predict spatial variation (H⁺) after measuring the inputs and outputs of major elements and their effect on H⁺ production. The results revealed that H⁺ ranged spatially from 0 to 16 keq H⁺ ha⁻¹ yr⁻¹. The most protons (H⁺) were produced by cassava, banana, and Irish potatoes systems with 12.0, 9.8, and 8.9 keq H⁺ ha⁻¹ yr⁻¹, respectively. The results of the 10-fold cross validation for the soil acidification model were a coefficient of determination (R²) of 0.6, a root mean square error (RMSE) of 2.1, and a mean absolute error (MAE) of 1.4. Net basic cation loss drives soil acidification under optimized fertilizer application and climate covariates had a higher relative importance than other covariates. Digital soil mapping can produce soil acidification maps for sustainable land use and management plans.