Key Laboratory of Arable Land Conservation Middle and Lower Reaches of Yangtse River

Wuhan, China

Key Laboratory of Arable Land Conservation Middle and Lower Reaches of Yangtse River

Wuhan, China
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Ren T.,Huazhong Agricultural University | Ren T.,Key Laboratory of Arable Land Conservation Middle and Lower Reaches of Yangtse River | Liu B.,Huazhong Agricultural University | Liu B.,Key Laboratory of Arable Land Conservation Middle and Lower Reaches of Yangtse River | And 8 more authors.
Field Crops Research | Year: 2017

Greater synchrony between populations and individual plants is dependent on the interactions between plant density and nitrogen (N) fertilization and is crucial to achieve food and environment security. A two-factor designed experiment of transplanted winter oilseed rape was conducted in the 2012–2013 and 2013–2014 growing seasons in central China. Five transplanted plant densities and six N rates were used to study the influences of plant density and N fertilizer application rate on seed yield, N uptake and apparent N surplus. Optimal plant density was the prerequisite to obtain high seed yield and the contribution of plant density to seed yield was minor when it exceeded the optimal density. N fertilization offset the compensatory effect between plant density and pods per plant, enhance seed yield further. Seed yield increased linearly with the increase of seed N uptake, however, it did not increase as the non-seed parts (stem + pod wall) N uptake increased when seed yield exceeded the critical values in the high plant density treatments. Higher plant densities promoted shoot N uptake and reduced soil mineral N residues and apparent N surplus in root zone. Compared with the recommended transplanted density in this region (9.0 × 104 plant ha−1), the optimal N fertilizer application rate could be cut down with 10.3%–23.7% for the target yield (3000 kg ha−1) and apparent N surplus would be declined with 41.5%–92.1% when the density suitably increased to 10.0–12.0 × 104 plant ha−1; On the contrary, when the density was decreased to 7.0–8.0 × 104 plant ha−1, increasing N fertilization rate with 13.9%–34.8% also could gain the target yield, but the apparent N surplus would increase with 58.1%–148.0%. Consequently, varied combinations between plant densities and N fertilizer application rate are useful to fulfill high seed yield. Reducing N fertilizer rates under suitable higher plant densities are the optimal N management strategy to achieve high yield with lower environment risks. © 2017 Elsevier B.V.


Yang Y.,Huazhong Agricultural University | Yang Y.,Key Laboratory of Arable Land Conservation Middle and Lower Reaches of Yangtse River | Christakos G.,Zhejiang University | Christakos G.,San Diego State University | And 4 more authors.
Environmental Pollution | Year: 2017

Assessing the space-time trends and detecting the sources of heavy metal accumulation in soils have important consequences in the prevention and treatment of soil heavy metal pollution. In this study, we collected soil samples in the eastern part of the Qingshan district, Wuhan city, Hubei Province, China, during the period 2010-2014. The Cd, Cu, Pb and Zn concentrations in soils exhibited a significant accumulation during 2010-2014. The spatiotemporal Kriging technique, based on a quantitative characterization of soil heavy metal concentration variations in terms of non-separable variogram models, was employed to estimate the spatiotemporal soil heavy metal distribution in the study region. Our findings showed that the Cd, Cu, and Zn concentrations have an obvious incremental tendency from the southwestern to the central part of the study region. However, the Pb concentrations exhibited an obvious tendency from the northern part to the central part of the region. Then, spatial overlay analysis was used to obtain absolute and relative concentration increments of adjacent 1- or 5-year periods during 2010-2014. The spatial distribution of soil heavy metal concentration increments showed that the larger increments occurred in the center of the study region. Lastly, the principal component analysis combined with the multiple linear regression method were employed to quantify the source apportionment of the soil heavy metal concentration increments in the region. Our results led to the conclusion that the sources of soil heavy metal concentration increments should be ascribed to industry, agriculture and traffic. In particular, 82.5% of soil heavy metal concentration increment during 2010-2014 was ascribed to industrial/agricultural activities sources.Using STK and SOA to obtain the spatial distribution of heavy metal concentration increments in soils.Using PCA-MLR to quantify the source apportionment of soil heavy metal concentration increments. © 2017 Elsevier Ltd.


Yang Y.,Huazhong Agricultural University | Yang Y.,Key Laboratory of Arable Land Conservation Middle and Lower Reaches of Yangtse River | Yang Y.,San Diego State University | Zhang C.,Huazhong Agricultural University | And 3 more authors.
Stochastic Environmental Research and Risk Assessment | Year: 2014

Using auxiliary information to improve the prediction accuracy of soil properties in a physically meaningful and technically efficient manner has been widely recognized in pedometrics. In this paper, we explored a novel technique to effectively integrate sampling data and auxiliary environmental information, including continuous and categorical variables, within the framework of the Bayesian maximum entropy (BME) theory. Soil samples and observed auxiliary variables were combined to generate probability distributions of the predicted soil variable at unsampled points. These probability distributions served as soft data of the BME theory at the unsampled locations, and, together with the hard data (sample points) were used in spatial BME prediction. To gain practical insight, the proposed approach was implemented in a real-world case study involving a dataset of soil total nitrogen (TN) contents in the Shayang County of the Hubei Province (China). Five terrain indices, soil types, and soil texture were used as auxiliary variables to generate soft data. Spatial distribution of soil total nitrogen was predicted by BME, regression kriging (RK) with auxiliary variables, and ordinary kriging (OK). The results of the prediction techniques were compared in terms of the Pearson correlation coefficient (r), mean error (ME), and root mean squared error (RMSE). These results showed that the BME predictions were less biased and more accurate than those of the kriging techniques. In sum, the present work extended the BME approach to implement certain kinds of auxiliary information in a rigorous and efficient manner. Our findings showed that the BME prediction technique involving the transformation of variables into soft data can improve prediction accuracy considerably, compared to other techniques currently in use, like RK and OK. © 2014 Springer-Verlag Berlin Heidelberg


Yang Y.,Huazhong Agricultural University | Yang Y.,Key Laboratory of Arable Land Conservation Middle and Lower Reaches of Yangtse River | Christakos G.,Zhejiang University | Christakos G.,San Diego State University
Environmental Science and Technology | Year: 2015

China experiences severe particulate matter (PM) pollution problems closely linked to its rapid economic growth. Advancing the understanding and characterization of spatiotemporal air pollution distribution is an area where improved quantitative methods are of great benefit to risk assessment and environmental policy. This work uses the Bayesian maximum entropy (BME) method to assess the space-time variability of PM2.5 concentrations and predict their distribution in the Shandong province, China. Daily PM2.5 concentrations obtained at air quality monitoring sites during 2014 were used. On the basis of the space-time PM2.5 distributions generated by BME, we performed three kinds of querying analysis to reveal the main distribution features. The results showed that the entire region of interest is seriously polluted (BME maps identified heavy pollution clusters during 2014). Quantitative characterization of pollution severity included both pollution level and duration. The number of days during which regional PM2.5 exceeded 75, 115, 150, and 250 μg m-3 varied: 43-253, 13-128, 4-66, and 0-15 days, respectively. The PM2.5 pattern exhibited an increasing trend from east to west, with the western part of Shandong being a heavily polluted area (PM2.5 exceeded 150 μg m-3 during long time periods). Pollution was much more serious during winter than during other seasons. Site indicators of PM2.5 pollution intensity and space-time variation were used to assess regional uncertainties and risks with their interpretation depending on the pollutant threshold. The observed PM2.5 concentrations exceeding a specified threshold increased almost linearly with increasing threshold value, whereas the relative probability of excess pollution decreased sharply with increasing threshold. © 2015 American Chemical Society.


Yang Y.,Huazhong Agricultural University | Yang Y.,Key Laboratory of Arable Land Conservation Middle and Lower Reaches of Yangtse River | Yang Y.,San Diego State University | Wu J.,Zhejiang University | Christakos G.,Zhejiang University
Ecological Indicators | Year: 2015

Soil heavy metal concentrations exhibit significant space-time trends due to their accumulation along the time axis and the varying distances from the pollution sources. Thus, concentration trends cannot be ignored when performing spatiotemporal soil heavy metal predictions in an area. In this work, datasets were used of soil cadmium (Cd) concentrations in the Qingshan district (Wuhan City, Hubei Province, China) sampled during the period 2010-2014. Spatiotemporal Kriging with four Trend models (STKT) and non-separable space-time correlation was implemented to assimilate multi-temporal data in the mapping of Cd distribution within the contaminated soil area. Soil Cd trends were represented by four different space-time polynomial functions, and a non-separable power function-exponential variogram model of Cd distribution was assumed. Plots of the predicted space-time Cd distributions revealed a marked tendency of the Cd concentrations over time to spread from the southwest part to the entire study area (higher soil Cd concentrations are found in the southwest part of the Qingshan area, whereas the temporal Cd trend is characterized by a constant increase from 2010 to 2014). Thus, the maps indicate that the entire study area is contaminated by Cd, a situation that seems to be stable over time. STKT can reduce prediction errors in practically and statistically significant ways. A numerical comparison of the STKT technique vs. the mainstream Spatiotemporal Ordinary Kriging (STOK) technique showed that STKT can perform better than STOK when the trend model's goodness of fit to the Cd data was satisfactory (producing minimal data fit error statistics), implying that adequate trend modeling is a key issue for space-time prediction accuracy purposes. In particular, quantitative results obtained at the Qingshan region showed that, by incorporating local Cd values and distance-based dependence structures the STKT techniques produced the best prediction error statistics, resulting in considerable prediction error reductions (the level of which depend on the trend model specification; e.g.; in the case of STKT with trend model 3 the improvement comparing to STOK was almost 30%). Future studies of Cd contamination in the region (sampling design optimization) can benefit from the results of the geostatistical analysis of the present paper (variogram and trend modeling, etc.). © 2015 Elsevier Ltd.


Yang Y.,Key Laboratory of Arable Land Conservation Middle and Lower Reaches of Yangtse River | Yang Y.,Huazhong Agricultural University | Zhang C.T.,Key Laboratory of Arable Land Conservation Middle and Lower Reaches of Yangtse River | Zhang C.T.,Huazhong Agricultural University | And 3 more authors.
Journal of Environmental Informatics | Year: 2015

The concern of this work is the systematic synthesis of site-specific samples and auxiliary information (including continuous and categorical variables) aiming at improving spatial prediction of natural attributes (soil properties, contaminant processes etc.). Bayesian Maximum Entropy (BME) is the theoretical support of the proposed synthesis. The significance of the synthesis is that it can increase the prediction accuracy of natural attributes in a physically meaningful and technically efficient manner. The spatial prediction approach is applied in a real world case study that combines soil organic matter (SOM) content samples with auxiliary information (terrain indices, soil types, and soil texture) to generate predictive maps. Prediction was affected by soil type and soil texture (prediction accuracy increased when categorical variables were included). In the same case study, the BME-based approach was compared with mainstream spatial statistics techniques, like Regression Kriging (RK) with auxiliary information, and hard data-driven Ordinary Kriging (OK). The numerical results demonstrated the superiority of the BME-based approach over the Kriging-based techniques, whereas it was found that some key BME parameters (counts of soft data, predicted variables categories, and continuous auxiliary variable categories) can have different effect on SOM prediction accuracy. The success of BME-based prediction relied heavily on finding adequate auxiliary information about the study situation. © 2015 ISEIS All rights reserved.


Yang Y.,Huazhong Agricultural University | Yang Y.,Key Laboratory of Arable Land Conservation Middle and Lower Reaches of Yangtse River | Christakos G.,Zhejiang University
Environmental Monitoring and Assessment | Year: 2015

Mapping the space–time distribution of heavy metals in soils plays a key role in contaminated site classification under conditions of in situ uncertainty, whereas uncertainty assessment is based on the quantification of the specific uncertainties in terms of exceedance probabilities. Geostatistical space-time kriging (STK) is increasingly used to estimate pollutant concentrations in soils. Sequential indicator simulation (SIS) technique is popular in uncertainty assessment of heavy metal contamination of soils. However, these techniques cannot handle multi-temporal data. In this work, spatiotemporal sequential indicator simulation (STSIS) based on an additive space–time semivariogram model (STSIS_A) and on a non-separable space–time semivariogram model (STSIS_NS) was used to assimilate multi-temporal data in the mapping and uncertainty assessment of heavy metal distributions in contaminated soils. Cu concentrations in soils sampled during the period 2010–2014 in the Qingshan district (Wuhan City, Hubei Province, China) were used as the experimental data set. Based on a number of STSIS realizations, we assessed different kinds of mapping uncertainty, including single-location uncertainty during 1 year and during multiple years, multi-location uncertainty during 1 year, and during multiple years. The comparison of the STSIS technique vs. SIS and STK techniques showed that STSIS performs better than both STK and SIS. © 2015, Springer International Publishing Switzerland.


Zhang C.,Huazhong Agricultural University | Zhang C.,Key Laboratory of Arable Land Conservation Middle and Lower Reaches of Yangtse River | Yang Y.,Huazhong Agricultural University | Yang Y.,Key Laboratory of Arable Land Conservation Middle and Lower Reaches of Yangtse River
Communications in Computer and Information Science | Year: 2013

In consideration of the correlation of soil properties and the surrounding environment, this paper proposed a method to compute histogram soft data based on soil-landscape model with soil attribute values of the sample sites and environmental factors data. The soft data was used in Bayesian Maximum Entropy (BME) to predict the spatial distribution of soil organic matter in Shayang County, Hubei Province, center China. The method of prediction was compared with the ordinary Kriging (OK) by mean error (ME) and mean squared error (MSE). Results showed that the BME predictions were more accurate and successfully estimates the degree of fluctuation in the observations. In this situation the method proposed by this paper to get soft data is applicative and the BME is an effective approach to improve the spatial distribution of soil properties prediction accuracy. © Springer-Verlag Berlin Heidelberg 2013.


Li J.,Huazhong Agricultural University | Li J.,Key Laboratory of Arable Land Conservation Middle and Lower Reaches of Yangtse River | Lu J.,Huazhong Agricultural University | Lu J.,Key Laboratory of Arable Land Conservation Middle and Lower Reaches of Yangtse River | And 8 more authors.
PLoS ONE | Year: 2014

Straw application can not only increase crop yields, improve soil structure and enrich soil fertility, but can also enhance water and nutrient retention. The aim of this study was to ascertain the relationships between straw decomposition and the release-adsorption processes of K+. This study increases the understanding of the roles played by agricultural crop residues in the soil environment, informs more effective straw recycling and provides a method for reducing potassium loss. The influence of straw decomposition on the K+ release rate in paddy soil under flooded condition was studied using incubation experiments, which indicated the decomposition process of rice straw could be divided into two main stages: (a) a rapid decomposition stage from 0 to 60 d and (b) a slow decomposition stage from 60 to 110 d. However, the characteristics of the straw potassium release were different from those of the overall straw decomposition, as 90% of total K was released by the third day of the study. The batches of the K sorption experiments showed that crop residues could adsorb K+ from the ambient environment, which was subject to decomposition periods and extra K+ concentration. In addition, a number of materials or binding sites were observed on straw residues using IR analysis, indicating possible coupling sites for K+ ions. The aqueous solution experiments indicated that raw straw could absorb water at 3.88 g g21, and this rate rose to its maximum 15 d after incubation. All of the experiments demonstrated that crop residues could absorb large amount of aqueous solution to preserve K+ indirectly during the initial decomposition period. These crop residues could also directly adsorb K+ via physical and chemical adsorption in the later period, allowing part of this K+ to be absorbed by plants for the next growing season. © 2014 Li et al.


Li J.,Huazhong Agricultural University | Li J.,Key Laboratory of Arable Land Conservation Middle and Lower Reaches of Yangtse River | Zhang W.,Huazhong Agricultural University | Zhang W.,Key Laboratory of Arable Land Conservation Middle and Lower Reaches of Yangtse River | And 6 more authors.
Applied Geochemistry | Year: 2014

The aim of this study was to investigate the dissolution and transformation characteristics of phyllosilicate under low molecular weight organic acids in the farmland environment (pH 4.0-8.0). Changes of dissolution and morphology of biotite were evaluated using chemical extraction experiments and in situ/ex situ atomic force microscopy (AFM) with fluids of citric acid (CA) solution at pH 4.0, 6.0, and 8.0. Results of extracting experiments show that CA solutions contributed to the release rate of potassium (K), silicon (Si), and aluminum (Al) from biotite relative to a control aqueous solution. In situ AFM observations indicate that the dissolution of biotite from the biotite (001) surface occurred on the terrace, segment, and fringe of pits, while new etch pits did not readily form on biotite (001) surfaces in aqueous solutions. However, dissolution rates of terraces can be greatly accelerated with the help of citrate. In pH 4.0 CA solution, 70. min dissolution reactions of biotite (001) surfaces result in more etch pits than in pH 6.0 and 8.0 solutions. In addition, the transformation of biotite occurred simultaneously with the dissolution process. Secondary coating was observed on the biotite (001) surface after 140. h of immersion in a weak acid environment. Thus, the protons have a dominant role in the dissolution process of biotite with organic (carboxyl) acting as a catalyst under acidic condition. Based on the theory of interactions on a water-mineral interface in a weak acid environment, dissolution of biotite starts from defect/kink sites on the surface, one layer by one layer, and develops along the [hk0] direction. A secondary coating that forms on the biotite (001) surface may restrain the formation and growth of etch pits, whereas this process may have a positive role on the stability of soil structure during long-term soil management. © 2014 Elsevier Ltd.

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