Spatial multi-scale variability of soil nutrients in relation to environmental factors in a typical agricultural region, eastern China

Variability analysis of soil properties, mapping, and crop test responses in Southern Ethiopia

Agricultural productivity is significantly impacted by soil properties, which vary spatially from a small to a larger area. This variation may be caused by a combination of intrinsic and extrinsic factors, including human activities like soil management practices. The aim of the current study was to analyze soil spatial variability, create a Digital Soil Map (DSM), and test map information with crop in Southern Ethiopia. A total of 18 geo-referenced surface soil samples at depth of 20 cm were collected. Selected soil Physico-chemical properties such as soil texture, pH, organic carbon (OC), total nitrogen (TN), available phosphorus (av. P), sulfur (S), exchangeable bases [calcium (Ca), magnesium (Mg), and potassium (K)], soil micronutrients [boron (B), copper (Cu), iron (Fe), manganese (Mn) and zinc (Zn)] and cation exchange capacity (CEC) were analyzed. The results revealed clay texture with a mean pH value of 4.6 (strong acidity). About 50% of essential nutrients [N, P, S, Ca, Mg, B & Fe] were deficient. The geostatistical analysis has shown that the best-fitted models were exponential for (OC, TN, available P, S, Mg, CEC, B, Fe, and Zn), spherical for (pH, Ca, Cu and Mn), and Gaussian for (C:N, K, K:Mg, and PBS). The range of all soil properties varied from 50 m to 84 m which was above the actual distance between soil samples (i.e., 46 m). The result showed that the spatial dependence values for soil properties of [OC, TN, CEC, PBS, ESP, and Cu]; [pH, C: N ratio, available P, S, Ca, Mg, K, Na, K: Mg ratio and Zn] and [B, Fe, M n] were strong (<25%]; weak (>75%) and moderate (25%-75%), respectively. Model performance using indicators such as prediction mean error (PME), root mean square standardized error (RMSSE), mean standard error (MSE), and root-mean-square error (RMSE) also confirmed the acceptable prediction. The DSM demonstrated the limitation of N, P, S, and B nutrients for intervention. The DSM information was tested under field conditions using haricot bean (Phaseolus vulgaris) with lime and organic fertilizers as treatments. The experiment consists of lime rates (0, 3, 6 t/ha), rhizobium inoculation (inoculated and non-inoculated), and fertilizer types (0, 150 kg ha^-1 NPSB, 5 t/ha vermi compost, 10t/ha farmyard manure (FYM)) in Randomized Complete Block Design with three replications. The result exhibited interaction effects of lime, inoculation, and fertilizer types significantly influenced (p < 0.05) biomass and grain yield of haricot beans. Rhizobium inoculation x 6t/ha lime x 150 kg ha^-1 NPSB recorded the maximum grain yield (3186.1 kg/ha) which was 26.3 fold over the non-treated soil (117 kg ha^-1). In conclusion, the DSM classified the area into distinct management zones which were tested with a crop trial. The results of the trial confirm the importance of site-specific nutrients/amendment application for sustainable soil management.

Effects of fertilizer under different dripline spacings on summer maize in northern China

Maximizing grain yields with effective fertilization technologies and minimizing nitrogen losses is essential in agroecosystems. In this research, we conducted a two-year field experiment to explore whether dripline spacing and fertilization rate would affect maize grain yield. Two dripline spacings (i.e., one drip line per row of maize with a row space of 60 cm and one drip line per two rows of maize) and two fertilization rates (i.e., high fertilization level: N, 180 kg ha⁻¹; P₂O₅, 90 kg ha⁻¹; and K₂O, 90 kg ha⁻¹ and low level: N, 139.5 kg ha⁻¹; P₂O₅, 76.5 kg ha⁻¹; and K₂O, 76.5 kg ha⁻¹) were employed in this research. The results showed that maize yield was significantly affected by both dripline spacing and fertilization rate. The maize yield was 10.2% higher in the treatment with one drip line per two rows than that in the treatment with one drip line per row. Maize yield increased by 10.9% at the high fertilization level compared to that at the low fertilization level. The quantity of cumulative ammonia volatilization was reduced by 15.1% with one drip line per two rows compared to that with one drip line per row, whereas it increased by 26.9% at the high fertilization level compared with that at the low fertilization level. These results indicated that one drip line per two rows with a high fertilization rate increased the yield and could reduce the environmental burden, which may be economically beneficial and environmentally sound for maize fertigation for green agricultural development.

Protocol for the use of legacy data and magnetic signature on soil mapping of São Paulo Central West, Brazil

The demand for information on the soil resource to support the establishment of public policies for land use and management has grown exponentially in the last years. However, there are still difficulties to the proper use of already existing information for soil mapping. Here we aimed to establish a protocol for soil mapping using legacy data, magnetic signature and soil attributes evaluation. A total of 493 soil samples were collected at 0-0.20 m in the geological domain of Western Plateau of São Paulo State. This work has three parts: First, we performed a classification analysis using soil mapping units (SMU) extracted from conventional soil map and Support Vector Machines algorithm (SVM). As covariates, we used categorical information, such as geology, dissection and landform maps. Second, we used soil attributes to perform a cluster analysis using k-means as partitioning method. To choose the optimal number of clusters, the same number of SMU showed in the conventional soil map (e.g. 34 clusters) were used. The last step was to compare soil and clusters maps predicted by SVM with the conventional soil map. Results showed good performance of SVM for both classifications (clusters and SMU), with overall accuracy of 0.60 and 0.90 respectively. In addition, the distribution of soil attributes within each cluster was more homogeneous and well distributed than within SMU, showing that is very possible to use numerical classification for soil mapping. Future soil surveys could use cluster analysis as a preliminary evaluation for better understanding of tropical soil variations.

Identification of the co-existence of low total organic carbon contents and low pH values in agricultural soil in north-central Europe using hot spot analysis based on GEMAS project data

Total organic carbon (TOC) contents in agricultural soil are presently receiving increased attention, not only because of their relationship to soil fertility, but also due to the sequestration of organic carbon in soil to reduce carbon dioxide emissions. In this research, the spatial patterns of TOC and its relationship with pH at the European scale were studied using hot spot analysis based on the agricultural soil results of the Geochemical Mapping of Agricultural Soil (GEMAS) project. The hot and cold spot maps revealed the overall spatial patterns showing a negative correlation between TOC contents and pH values in European agricultural soil. High TOC contents accompanying low pH values in the north-eastern part of Europe (e.g., Fennoscandia), and low TOC with high pH values in the southern part (e.g., Spain, Italy, Balkan countries). A special feature of co-existence of comparatively low TOC contents and low pH values in north-central Europe was also identified on hot and cold spot analysis maps. It has been found that these patterns are strongly related to the high concentration of SiO₂ (quartz) in the coarse-textured glacial sediments in north-central Europe. The hot spot analysis was effective, therefore, in highlighting the spatial patterns of TOC in European agricultural soil and helpful to identify hidden patterns.

An integrated approach to identify quantitative sources and hazardous areas of heavy metals in soils

Identifying quantitative sources and hazardous areas of heavy metals is a crucial issue for soil management. For this purpose, an integrated approach composed of finite mixture distribution modeling (FMDM), positive matrix factorization (PMF) and sequential Gaussian co-simulation (SGCS) was proposed. FMDM was used to establish background standards and pollution thresholds. PMF supported by FMDM background standards was applied to estimate the source apportionment. Hazardous areas of single metals were delineated using SGCS with FMDM pollution thresholds and uncertainty analysis, and overall hazardous areas were defined by the presence of multiple metals. This integrated approach was applied to a dataset of seven metals as a case study. FMDM indicated that the distributions of Cr, Cu, Ni, and Zn were fitted to two-dimensional mixture distributions, representing a background distribution and a moderately polluted distribution. The distributions of Cd, Hg, and Pb were composed of a three-component lognormal mixture distribution, corresponding to the background, moderate, and high pollution distributions. Three sources were apportioned. Cr, Cu, Ni, and Zn were dominated by parent materials. Parent materials contributed 52.6%, 45.8%, and 81.9% of Cd, Hg, and Pb concentrations, respectively. Human emissions from coal combustion, industrial work and traffic had significant influences on Hg, Cd, and Pb, with contributions of 49.8%, 26.9%, and 15.6%, respectively. Agricultural practices were exclusively associated with 20.5% of Cd. Overall, hazardous areas exceeding moderate pollution thresholds covered 17.4% of the total area, corresponding to urban areas and industrial sites, whereas overall hazardous areas above high pollution thresholds were limited to 0.01% of the total area.

Multivariate receptor models and robust geostatistics to estimate source apportionment of heavy metals in soils

Absolute principal component score/multiple linear regression (APCS/MLR) and positive matrix factorization (PMF) were applied to a dataset consisting of 10 heavy metals in 300 surface soils samples. Robust geostatistics were used to delineate and compare the factors derived from these two receptor models. Both APCS/MLR and PMF afforded three similar source factors with comparable contributions, but APCS/MLR had some negative and unidentified contributions; thus, PMF, with its optimal non-negativity results, was adopted for source apportionment. Experimental variograms for each factor from two receptor models were built using classical Matheron's and three robust estimators. The best association of experimental variograms fitted to theoretical models differed between the corresponding APCS and PMF-factors. However, kriged interpolation indicated that the corresponding APCS and PMF-factor showed similar spatial variability. Based on PMF and robust geostatistics, three sources of 10 heavy metals in Guangrao were determined. As, Co, Cr, Cu, Mn, Ni, Zn, and partially Hg, Pb, Cd originated from natural source. The factor grouping these heavy metals showed consistent distribution with parent material map. 43.1% of Hg and 13.2% of Pb were related to atmosphere deposition of human inputs, with high values of their association patterns being located around urban areas. 29.6% concentration of Cd was associated with agricultural practice, and the hotspot coincided with the spatial distribution of vegetable-producing soils. Overall, natural source, atmosphere deposition of human emissions, and agricultural practices, explained 81.1%, 7.3%, and 11.6% of the total of 10 heavy metals concentrations, respectively. Receptor models coupled with robust geostatistics could successfully estimate the source apportionment of heavy metals in soils.

Future Impacts of Climate Change and Land Use on Multiple Ecosystem Services in a Rapidly Urbanizing Agricultural Basin, China

Ecosystem services (ESs) in rapidly urbanizing agricultural basins are vulnerable to environmental changes. Adequately understanding the driving forces and the dynamics of ESs related to water quantity and quality can provide a basis for making sound management decisions on the development of basins. Here, we explored the impacts of future land use and climate changes on four ESs: nitrogen and phosphorous purification, water supply, and soil retention services in the Taihu Basin region of eastern China. Spatially explicit methods, a cellular automata-Markov (CA-Markov) model and the delta downscaling method were used to quantify the ESs, simulate land use changes, and project future climate changes, respectively. We built a business-as-usual land use scenario, representative concentration pathways (RCPs) scenarios for climate change, as well as a combined land use and climate change scenario to analyze the changes in the drivers and the responses of ESs. The results showed the following: (1) future land use changes would significantly enhance the nitrogen purification service while reducing the phosphorus purification service compared to other services; (2) climate change would have substantial effects on water supply and soil retention, but these impacts would vary with different RCPs scenarios during three future periods; and (3) the combined scenarios of both drivers would obviously influence all ESs and lead to a nitrogen purification service that was different from the other three services. Moreover, the policy implications of the results were discussed. The findings can help guide the creation of policies for land structure and patterns, climate change adaptation, and ecosystem-based management to promote the sustainable development of watersheds at the regional scale.

Multi-scale analysis of heavy metals sources in soils of Jiangsu Coast, Eastern China

Since the development of Jiangsu Coast was proposed as a national strategy by the Chinese Government in 2009, Jiangsu Coast has been experiencing rapid and intensive development in industry and tidal flat reclamation, which has inevitably led to the accumulation of heavy metals in its soils. A total of 239 samples (0-20 cm) were collected from topsoils of Jiangsu Coast, and their concentrations of Cd, Cr, Cu, Hg, Ni, Pb, and Zn were determined. Factorial kriging was applied to examine the scale-dependent correlations between heavy metals and to generate the spatial components at multiple scales, and multivariate stepwise regression was used to explore the relationships between the spatial multi-scale components of heavy metals and environmental factors. Linear model of co-regionalization (LMC) fitting indicated that the multi-scale variation comprised a nugget effect, an exponential structure with a range of 15 km (local scale), and a spherical structure with a range of 135 km (regional scale). The spatial correlations of seven heavy metals depended on their spatial scales, and their correlations increased with the increasing scales. Spatial variations in Cr and Ni were associated with natural geochemical sources on both local and regional scales. Parent material influenced the basic spatial variations in Cd, Cu, Pb and Zn on both local and regional scales, but human activity also contributed to the spatial variations in these four metals. The human inputs of Cd, Cu, Pb, and Zn differed on these two scales. Hg was dominated by industrial emissions and agricultural practices on both scales.

Lateral heterogeneity of soil physicochemical properties in riparian zones after agricultural abandonment

The study aimed to identify the lateral heterogeneity of soil physicochemical properties in riparian zones, and its underlying drivers during natural restoration after agricultural abandonment. Abandoned farmlands, after 5-year natural restoration, within 500 m from the edges on both sides of Liaohe River were selected as the study area. Soil physicochemical properties of four lateral buffers (<10 m, 10~100 m, 100~300 m, and >300 m from river edge, respectively) along riparian zones were measured. The results showed that riparian soils were characterized by high sand content (78.88%~96.52%) and poor soil nutrients. Soil silt content, organic carbon (OC), cation exchange capacity (CEC), total nitrogen (TN), and available nitrogen (AN) increased laterally with increasing distance from river edge, while soil sand content decreased. Total phosphorus (TP) and available phosphorus (AP) are not spatially autocorrelated. Soil OC, TN, AN, and CEC along upstream and midstream reaches showed negative spatial autocorrelation along the lateral gradients, and positive along downstream reach. Altitude, distance from river edge and distance from nearest farmland were the pronounced factors affecting soil physicochemical properties in this study.

Spatial multi-scale relationships of ecosystem services: A case study using a geostatistical methodology

Adequately understanding the spatial multi-scale relationships of ecosystem services (ES) is an important step for environmental management decision-making. Here, we used spatially explicit methods to estimate five critical ES (nitrogen and phosphorous purifications, crop production, water supply and soil retention) related to non-point source (NPS) pollution in the Taihu Basin region of eastern China. Then a factorial kriging analysis and stepwise multiple regression were performed to identify the spatial multi-scale relationships of ES and their dominant factors at each scale. The spatial variations in ES were characterized at the 12 km and 83 km scales and the result indicated that the relationships of these services were scale dependent. It was inferred that at the 12 km scale, ES were controlled by anthropogenic activities and their relationships were dependent on socio-economic factors. At the 83 km scale, we suggested that ES were primarily dominated by the physical environment. Moreover, the policy implications of ES relationships and their dominant factors were discussed for the multi-level governance of NPS pollution. Overall, this study presents an optimized approach to identifying ES relationships at multiple spatial scales and illustrates how appropriate information can help guide water management.

Two-dimensional empirical mode decomposition of heavy metal spatial variation in agricultural soils, Southeast China

The distribution of heavy metals in agricultural soils is affected by various anthropogenic activities and environmental factors occurring at different spatial scales. This paper introduced the two-dimensional empirical mode decomposition (2D-EMD) to separate the spatial variability in soil heavy metals into different scales. Geostatistics and multivariate analysis were also utilized to quantify their spatial structure and identify their potential influencing factors. The study was conducted in an arable land in southeastern China where 260 surface soil samples were collected and measured for total contents of cadmium (Cd_total), mercury (Hg_total), and sulfur (TS); pH; and soil organic carbon content (SOC). The results showed that both Cd_total and Hg_total had high coefficients of variation. The overall variation in all five soil variables was separated into three intrinsic mode functions (IMFs) and spatial residues. All three IMFs had short-range spatial correlations (1-8 km), while the spatial residues had moderate-large spatial ranges (13-39 km). IMF1 of Cd_total was strongly correlated with IMF1 of SOC and TS, which was consistent with the principal component analysis. This indicated that IMF1 of Cd_total represented local variations which were influenced by agricultural activities. IMFs of Hg_total showed clustered distributions in the study area, with IMF1 and IMF2 of Hg_total correlated in one principal component, and IMF3 of Hg_total and IMF3 of soil pH in another component. This indicated that all three IMFs of Hg_total might be influenced by different industrial activities or different pathways of the same industrial activities. The residues of Cd_total and Hg_total, representing the regional trends, only accounted for 26% of the total variance, whereas IMF1 contributed about half of the total variance. It can be concluded that agricultural activities and industrial activities were the dominant contributors of the overall variations in Cd_total and Hg_total in the study area, respectively.

Distinguishing anthropogenic and natural sources of trace elements in soils undergoing recent 10-year rapid urbanization: a case of Donggang, Eastern China

A total of 59 samples consisting of 45 topsoils samples and 14 subsoils samples were collected from urban soils of Donggang and were analyzed for soil properties and 12 trace elements. The mean contents of As, Cd, Co, Cr, Cu, Hg, Mn, Ni, Pb, Se, V, and Zn in topsoils were 5.76, 0.20, 9.88, 44.82, 17.69, 0.05, 578.96, 19.98, 28.38, 0.21, 58.88, and 58.32 mg kg(-1), respectively. The mean enrichment factor results suggested that Hg, Cd, Pb, Cu, Se, and Zn were enriched in topsoils compared with subsoils. Spatial distribution maps of trace elements indicated that Hg, Cd, Pb, Cu, Se, and Zn had similar patterns, with the highest values in the industrial region. There were no significant associations displayed between spatial distributions of As, Co, Cr, Mn, Ni, and V and the industrial region. Through correlation analysis, stepwise regression analysis, and redundancy analysis, three main sources of 12 trace elements were identified. Cd, Hg, and Se originated from industrial emissions and coal combustion, and As, Co, Cr, Mn, Ni, and V had a lithogenic origin. The combination of human activities and natural sources contributed to the contents of Cu, Pb, and Zn, and the human activities included industrial and traffic emissions.

Multivariate geostatistical analyses of heavy metals in soils: spatial multi-scale variations in Wulian, Eastern China

The objective of this study was to examine spatial multi-scale variability of six heavy metals (Cd, Cr, Cu, Ni, Pb and Zn) in relation to environmental factors in Wulian, Eastern China. Factorial kriging analysis (FKA) was applied to a data set consisting of 432 topsoils. We found that most of the heavy metal contents in soils did not exceed the guideline values of Environmental Quality Standard for Soils (EQSS) in China. Through linear model of coregionalization (LMC) fitting, spatial variation in six heavy metals could be grouped into one nugget effect, and two sphere structures with ranges of 6km (local scale) and 14km (regional scale). Spatial correlations among six heavy metals depended on local or regional scales. The high correlations between Cr, Ni and among Cd, Cu, Pb, Zn were found regardless of the spatial scale, while correlations of Cr and Ni with other four metals decreased with increasing spatial scale. Spatial variation of Cr and Ni was related to parent material at both local and regional scales, and was derived from natural sources. Mining activity was observed to affect the spatial variation of Cd, Cu, Pb and Zn at local scale, while parent material dominated spatial variation of those metals at regional scale. However, agricultural practices and human activity in urban area did not alter spatial variation of heavy metals in soils. It could be concluded that human influence on heavy metals variation was noted on local scale, and parent material had greater influence on spatial variation of heavy metals at both local and regional scales.