Weijun Fu

Contamination and Spatial Variation of Heavy Metals in the Soil-Rice System in Nanxun County, Southeastern China

There is an increasing concern about heavy metal contamination in farmland in China and worldwide. In order to reveal the spatial features of heavy metals in the soil-rice system, soil and rice samples were collected from Nanxun, Southeastern China. Compared with the guideline values, elevated concentrations of heavy metals in soils were observed, while heavy metals in rice still remained at a safe level. Heavy metals in soils and rice had moderate to strong spatial dependence (nugget/sill ratios: 13.2% to 49.9%). The spatial distribution of copper (Cu), nickel (Ni), lead (Pb) and zinc (Zn) in soils illustrated that their high concentrations were located in the southeast part. The high concentrations of cadmium (Cd) in soils were observed in the northeast part. The accumulation of all the studied metals is related to the long-term application of agrochemicals and industrial activities. Heavy metals in rice showed different spatial distribution patterns. Cross-correlograms were produced to quantitatively determine the spatial correlation between soil properties and heavy metals composition in rice. The pH and soil organic matter had significant spatial correlations with the concentration of heavy metals in rice. Most of the selected variables had clear spatial correlation ranges for heavy metals in rice, which could be further applied to divide agricultural management zones.

Effects of biochar application in forest ecosystems on soil properties and greenhouse gas emissions: a review

PurposeForests play a critical role in terrestrial ecosystem carbon cycling and the mitigation of global climate change. Intensive forest management and global climate change have had negative impacts on the quality of forest soils via soil acidification, reduction of soil organic carbon content, deterioration of soil biological properties, and reduction of soil biodiversity. The role of biochar in improving soil properties and the mitigation of greenhouse gas (GHG) emissions has been extensively documented in agricultural soils, while the effect of biochar application on forest soils remains poorly understood. Here, we review and summarize the available literature on the effects of biochar on soil properties and GHG emissions in forest soils.Materials and methodsThis review focuses on (1) the effect of biochar application on soil physical, chemical, and microbial properties in forest ecosystems; (2) the effect of biochar application on soil GHG emissions in forest ecosystems; and (3) knowledge gaps concerning the effect of biochar application on biogeochemical and ecological processes in forest soils.Results and discussionBiochar application to forests generally increases soil porosity, soil moisture retention, and aggregate stability while reducing soil bulk density. In addition, it typically enhances soil chemical properties including pH, organic carbon stock, cation exchange capacity, and the concentration of available phosphorous and potassium. Further, biochar application alters microbial community structure in forest soils, while the increase of soil microbial biomass is only a short-term effect of biochar application. Biochar effects on GHG emissions have been shown to be variable as reflected in significantly decreasing soil N2O emissions, increasing soil CH4 uptake, and complex (negative, positive, or negligible) changes of soil CO2 emissions. Moreover, all of the aforementioned effects are biochar-, soil-, and plant-specific.ConclusionsThe application of biochars to forest soils generally results in the improvement of soil physical, chemical, and microbial properties while also mitigating soil GHG emissions. Therefore, we propose that the application of biochar in forest soils has considerable advantages, and this is especially true for plantation soils with low fertility.

Using GIS and Geostatistics to Optimize Soil Phosphorus and Magnesium Sampling in Temperate Grassland

Abstract Soil sampling design is an important issue for agricultural management and environmental monitoring. In this study, a total of 537 soil samples were collected based on a 30 × 30–m grid from a permanent dairy farm in southeastern Ireland. Five different subsample experiments at lower densities based on the original data set were performed to study the optimal soil sampling design for soil P and Mg using geostatistics and a GIS (geographical information system). Soil P ranged from 1.3 to 35.7 mg L−1, with a CV value of 0.68. Soil Mg ranged from 134.7 to 685.2 mg L−1, with a small CV value of 0.28. Soil P followed neither a normal nor a lognormal distribution. Box-Cox transformation was applied to achieve normality. On the other hand, soil Mg followed a normal distribution, as did its subdata. For soil P, an omnidirectional spherical model was used to describe the spatial autocorrelation. For soil Mg, a nested model (an exponential model combined with a Gaussian model) was used to fit the variograms. Further soil P interpolated maps revealed that soil grid sampling interval could increase to 90 m without a significant loss of spatial information, whereas soil Mg sampling interval could increase to 120 m, confirming that soil Mg had much stronger spatial structure than soil P. According to this study, a grid of 90 × 90 m was recommended for soil sampling, which was confirmed in other practical grassland farms. The spatial structure information was very useful to optimize soil sampling design for practical grassland management.

Field-scale variability of soil test phosphorus and other nutrients in grasslands under long-term agricultural managements

Field-scale variation of soil nutrients in grassland is becoming important because of the use of soil-nutrient information as a basis for policies such as the recently introduced EU Nitrates Directive. This study investigates the field-scale variability of soil-test phosphorus (STP) and other nutrients in two grasslands with a long-term history of poultry litter application. Two fields (field 1 for silage and field 2 for grazing pasture) were selected, and soil samples were collected based on 12 m by 12 m (field 1) and 15 m by 15 m (field 2) grids. Data were analysed using conventional statistics, geostatistics, and a geographic information system (GIS). In field 1, STP values ranged from 12.4 to 90 mg L–1 (average 38.5 mg L–1). In field 2, STP values ranged from 4.3 to 130.0 mg L–1 (average 21.4 mg L–1). Attention should be paid to long-term poultry application, as the average STP values in both fields were much greater than the recommended agronomic optimum STP status in Ireland of 8 mg L–1. Coefficient of variation values of soil nutrients in field 2 were much higher than those in field 1. Log-transformation and Box–Cox transformation were applied to achieve normality. Statistically significant (P < 0.01), positive correlations between P and other nutrients were found in both fields. Exponential and spherical models were fitted to the experimental variograms of STP in fields 1 and 2, respectively. Compared with the counterparts in field 1, soil nutrients in field 2 had larger ‘nugget-to-sill’ values, revealing that sheep grazing could weaken the spatial auto-correlation of soil nutrients. A grid of 60 m by 60 m was recommended for soil sampling in grassland, based on this study. High STP concentrations in field 1 were in the north-eastern side, which was related to uneven poultry litter application. Strong spatial similarity of low STP, magnesium, and pH values in their spatial distribution were found in field 2, confirming their strong statistical correlation.

Effects of Inorganic and Organic Fertilizers on Soil CO2 Efflux and Labile Organic Carbon Pools in an Intensively Managed Moso Bamboo (Phyllostachys pubescens) Plantation in Subtropical China

ABSTRACT Impact of combined application of inorganic and organic fertilizers on soil carbon dioxide (CO2) emission is poorly understood. We investigated the effects of inorganic fertilizer (IF), organic fertilizer (OF), and a mixture of organic and inorganic fertilizers (OIF) applications on the dynamics of soil CO2 efflux in intensively managed Moso bamboo plantations. Soil CO2 efflux and concentrations of water soluble organic C (WSOC) and microbial biomass C (MBC) in the IF treatment were higher than those in the control but lower than those in the OF and OIF treatments. Both OF and OIF treatments increased the SOC stock. Strong exponential relationships (p < 0.01) between soil temperature and CO2 efflux were observed in all treatments. Soil CO2 efflux in all four treatments was correlated with WSOC (p < 0.05) but not with MBC. We concluded the combined approach can possibly contribute to increasing the level of SOC stock in intensively managed plantations.

Effects of intercropping grasses on soil organic carbon and microbial community functional diversity under Chinese hickory (Carya cathayensis Sarg.) stands

Chinese hickory (Carya cathayensis Sarg.) is a woody nut and oil tree from China. Intensive management including heavy application of chemical fertiliser and long-term application of herbicides has resulted in serious soil loss and degradation. This study aimed to test the hypothesis that intercropping in the soil under Chinese hickory stands may improve soil fertility and microbial community functional diversity. A field experiment consisting of four treatments (clean tillage; intercropping rape (Brassica rapa L.), ryegrass (Lolium perenne L.) or Chinese milk vetch (Astragalus sinicus L.) was conducted to study the effects of intercropping on soil organic carbon (SOC) structure and microbial community functional diversity under C. cathayensis stand, by means of 13C-nuclear magnetic resonance (NMR), and EcoPlates incubated at 25°C. After 4 years of treatment, intercropping increased available nitrogen (N), phosphorus and potassium in the soil by 25.1–54.2, 4.2–6.0 and 0–22.5 mg kg–1, respectively, relative to the clean tillage treatment; intercropping rape, ryegrass and Chinese milk vetch increased SOC, microbial biomass C (MBC), and water-soluble organic C (WOC) by 23.1–24.7, 138.6–159.7 and 56.2–69.5% (P < 0.05), respectively. The structure of SOC was also greatly changed by intercropping treatments. Intercropping increased carbonyl C by 29.9–36.9% (P < 0.05) and decreased alkyl C, O-alkyl C and aromatic C by 10.0–16.4, 18.9–20.9 and 10.5–16.6% (P < 0.05), respectively. Intercropping markedly improved microbial community functional diversity, which is characterised by increases in average well-colour development (AWCD), Shannon index and evenness index. Correlation analysis showed significant positive correlations among microbial biomass N, water-soluble organic N, SOC, WOC, MBC and AWCD (P < 0.05 or P < 0.01). The results demonstrate that sod cultivation is an effective soil management practice that improves soil quality and eliminates detrimental effects of clean tillage in Chinese hickory production.

Variation of soil P and other nutrients in a long-term grazed grassland P experiment field

Field-scale variation of soil phosphorus (P) information is very important for P fertilizer application and its soil sampling design in grassland. A total of 108 soil samples were collected from a long-term (41 years) grazed grassland P experiment field at Teagasc, Johnstown Castle, Wexford, Ireland, in March 2009. There were six P treatments (P0-0, P0-30, P15-15, P15-5, P30-30, and P30-0) since 1968, with changes since 1999. Each treatment had 6 replicate plots (a total of 36 plots, 3 soil samples per plot). The samples were analyzed for available (Morgan’s) P, potassium (K), magnesium (Mg), lime requirement (LR), and pH. The highest mean available P concentration was found in the P30-30 (30 kg P ha−1 pre- and post-1998) plots, and the lowest mean available P concentration was found in the P0-0 (no P fertilizer since 1968) plots. Significant differences of mean P, Mg, LR, and pH values in different treatments were observed. There was a positive proportional effect for both the 36 plots and the 6 treatments for the P data: the local standard deviation increased with the increase of local mean. The proportional effect should be considered in order to optimize sampling design. Fewer samples can reflect soil P status in fields with low soil P levels, while more attention should be paid to the fields with high P levels in order to reduce environmental consequences of uniform applications.

Using ArcGIS and Geostatistics to Study Spatial Pattern of Forest Litter Carbon Density in Zhejiang Province, China

A total of 840 forest litter samples were collected based on a 4-km (south–north)×6-km (east–west)-grid system in Zhejiang province. Traditional statistics and geostatistics were applied to study the spatial pattern of forest litter carbon density in an ArcGIS environment. Forest litter carbon density values were very variable, ranging from 10.2 to 4,289.8kghm−2, with an average of 1,627.1kghm−2. Spherical model was chosen to fit the experimental semi-variogram. The high nugget/sill (0.6275) value revealed that random variation played a key role in spatial heterogeneity of forest litter carbon density; human activities and land management further weaken its spatial correlation. Forest litter carbon density in Zhejiang province was above the average level of forest in China. High forest litter carbon values were mainly distributed in hilly region in Zhejiang, while HangJiaHu Plain, NingShao Plain, and coastal areas had low forest litter carbon density due to low forest coverage.