Spatial variability of soil nutrients in forest areas: A case study from subtropical China

Abstract

Soil nitrogen (N), phosphorus (P), potassium (K), and pH are important determinants and indicators of soil fertility and quality. Especially in forest, these soil properties are highly variable in space and time. However, more information on these properties in subtropical forests is needed to improve soil nutrients management and to sustain the productivity of subtropical forests. We collected a total of 838 forest topsoil samples (0–30 cm in depth) based on a 4‐km (E‐W) × 6‐km (S‐N) grid system in subtropical China. Geostatistics (i.e., global and local Moran's I) and geographical information system (GIS) techniques were applied to reveal the spatial distribution of N, P, and K contents and pH in subtropical forest soils. All the measured properties were highly variable. Soil N contents ranged from 9.55 mg kg−1 to 862.40 mg kg−1, P contents from 0.10 mg kg−1 to 195.40 mg kg−1, K contents from 10.00 mg kg−1 to 390.00 mg kg−1, and pH values from 2.69 to 8.09. The soil N, P and K contents correlated positively with soil organic carbon (SOC), while soil pH correlated negatively with SOC (P < 0.05). Global Moran's I revealed that soil N, P, and K contents and pH had significant positive spatial autocorrelations, and clear spatial patterns were identified based on local Moran's I. An exponential model was the best fitted model for soil N, P, K contents and pH. Spatial dependence was moderate for all studied soil variables, indicating that both intrinsic and extrinsic factors played a vital role in spatial heterogeneity. Distribution maps showed that soil N and K had similar spatial distribution patterns: a low value area surrounded by bands of higher values. A large‐scale low content area was observed in the spatial distribution map of soil P. An opposite trend was found for the spatial pattern of soil pH. The spatial distribution maps provide useful information for sustainable forest and environment management in subtropical China.

Local community assembly mechanisms shape soil bacterial β diversity patterns along a latitudinal gradient

“…Especially, we found that a large portion (38.1-77.7%) of the variation in β deviation was unexplained by environmental and spatial factors. This is consistent with previous studies 13,38 in which 55.2-73% of the variation in soil microbial community composition and diversity was unexplained. Interestingly, we also found that the unexplained portion was greater in regions with relatively low environmental heterogeneity (36-45°N) than in regions with high environmental heterogeneity (18°, 29-33°, and 47°and 53°N).…”

Biomass and Nutrients Variation of Chinese Fir Rooted Cuttings under Conventional and Exponential Fertilization Regimes of Nitrogen

“…This is because when N is deficient, plants increase the proportion of root biomass to increase the N uptake capacity of the roots. The biomass of stems, leaves, and whole plants treated with exponential fertilization showed a trend of increasing first and then decreasing with the increase of total N application rate, indicating that proper fertilization promoted the growth of rooted cuttings [23], significantly increased the accumulation of dry matter in seedlings [14,36], while excessive N application caused slight poison to seedlings [15,21,[37][38][39][40], further impeded the seedling growth and biomass accumulation [41,42]. The highest ratio (1.90) of aboveground biomass/belowground biomass at the final harvest occurred with EF2, the lowest (0.76) was attained with the CK, illustrating that ample and stable nutrients supply in the growing medium tended to enhance shoot growth more than root growth [43][44][45].…”

Spatial Patterns of Potentially Hazardous Metals in Soils of Lin’an City, Southeastern China

“…Geostatistics is based on the theory of regionalized variables [33]. A semi-variogram is chosen to quantify the spatial variation of an environmental variables and derives essential input parameters for kriging interpolation [34,35]. Geostatistics is widely applied to study environmental variables, which contain both random and structural features in their spatial distributions [36].…”