De-Cheng Li

Application of rare-earth elements in the agriculture of China and its environmental behavior in soil

Rare-earth elements (REEs) have been used in fertilizers in the agriculture of China for about 20 years. They have been shown to be beneficial elements for plants. For example, they have improved the yield and quality for several kinds of crops. This paper reviews the current literature on studies of REEs being used as fertilizers. Some studies have focused on the effects of REEs on metabolic nutrients, photosynthesis and stress resistance of plants. Other studies have shown that the environmental behaviors of REEs in soil are dominated by their low solubility. Fluorides, carbonates, phosphates and hydroxides may form neutral complexes containing REEs with a low solubility. The amount of extraneous REEs demonstrate the following relationship: residual» bound to organic matter> bound to Fe-Mn oxides> bound to carbonate»exchangeable and water soluble forms. The adsorption capacity of REEs depends on the clay type and the content of amorphous and manganese oxides, whereas the desorption of REEs is usually very low. At the end of the paper, authors discuss the needs for future environmental research on REEs, which would shed new light on the effects of REEs on agriculture, environment and human health.

Application of rare-earth elements in the agriculture of china and its environmental behavior in soil

Rare-earth elements (REEs) have been used in fertilizers in the agriculture of China for about 20 years. They have been shown to be beneficial elements for plants. For example, they have improved the yield and quality for several kinds of crops. This paper reviews the current literature on studies of REEs being used as fertilizers. Some studies have focused on the effects of REEs on metabolic nutrients, photosynthesis and stress resistance of plants. Other studies have shown that the environmental behaviors of REEs in soil are dominated by their low solubility. Fluorides, carbonates, phosphates and hydroxides may form neutral complexes containing REEs with a low solubility. The amount of extraneous REEs demonstrate the following relationship: residual>>bound to organic matter>bound to Fe-Mn oxides>bound to carbonate>>exchangeable and water soluble forms. The adsorption capacity of REEs depends on the clay type and the content of amorphous and manganese oxides, whereas the desorption of REEs is usually very low. At the end of the paper, authors discussed the needs for future environmental research on REEs, which would shed new light on the effects of REEs on agriculture, environment and human health.

Effect of exogenous selenium supply on photosynthesis, Na + accumulation and antioxidative capacity of maize ( Zea mays L.) under salinity stress

The mechanism of selenium-mediated salt tolerance has not been fully clarified. This study investigated the possible role of selenium (Se) in regulating maize salt tolerance. A pot experiment was conducted to investigate the role of Se (0, 1, 5 and 25 μM Na2SeO3) in photosynthesis, antioxidative capacity and ion homeostasis in maize under salinity. The results showed that Se (1 μM) relieved the salt-induced inhibitory effects on the plant growth and development of 15-day-old maize plants. Se application (1 μM) also increased the net photosynthetic rate and alleviated the damage to chloroplast ultrastructure induced by NaCl. The superoxide dismutase (SOD) and ascorbate peroxidase (APX) activities were increased, and ZmMPK5, ZmMPK7 and ZmCPK11 were markedly up-regulated in the roots of Se-treated plants, likely contributing to the improvement of antioxidant defence systems under salinity. Moreover, 1 μM Se increased K+ in the shoots while decreasing Na+ in the roots, indicating that Se up-regulates ZmNHX1 in the roots, which may be involved in Na+ compartmentalisation under salinity. The findings from this single experiment require repetition together with measurement of reactive oxygen species (ROS), but nevertheless suggest that exogenous Se alleviates salt stress in maize via the improvement of photosynthetic capacity, the activities of antioxidant enzymes and the regulation of Na+ homeostasis.

Precise estimation of soil organic carbon stocks in the northeast Tibetan Plateau.

There is a need for accurate estimate of soil organic carbon (SOC) stocks for understanding the role of alpine soils in the global carbon cycle. We tested a method for mapping digitally the continuous distribution of the SOC stock in three dimensions in the northeast of the Tibetan Plateau. The approach integrated the spatial distribution of the mattic epipedon which is a special surface horizon widespread and rich in organic matter in Tibetan grasslands. Prediction models resulted in high prediction accuracy. An average SOC stock in the mattic epipedon was estimated to be 4.99 kg m−2 in a mean depth of 14 cm. The amounts of SOC in the mattic epipedon, the upper 30 cm and 50 cm accounted for about 21%, 80% and 89%, respectively, of the total SOC stock in the upper 1 m depth. Compared with previous estimates, our approach resulted in more reliable predictions. The mattic epipedon was proven to be an important factor for modelling the realistic distribution of the SOC stock in Tibetan grasslands. Vegetation-related covariates have the most important influence on the distribution of the mattic epipedon and the SOC stock in the alpine grassland soils of northeast Tibetan Plateau.

Modeling spatio-temporal distribution of soil moisture by deep learning-based cellular automata model

Soil moisture content (SMC) is a key hydrological parameter in agriculture, meteorology and climate change, and understanding of spatio-temporal distributions of SMC in farmlands is important to address the precise irrigation scheduling. However, the hybrid interaction of static and dynamic environmental parameters makes it particularly difficult to accurately and reliably model the distribution of SMC. At present, deep learning wins numerous contests in machine learning and hence deep belief network (DBN), a breakthrough in deep learning is trained to extract the transition functions for the simulation of the cell state changes. In this study, we used a novel macroscopic cellular automata (MCA) model by combining DBN to predict the SMC over an irrigated corn field (an area of 22 km2) in the Zhangye oasis, Northwest China. Static and dynamic environmental variables were prepared with regard to the complex hydrological processes. The widely used neural network, multi-layer perceptron (MLP), was utilized for comparison to DBN. The hybrid models (MLP-MCA and DBN-MCA) were calibrated and validated on SMC data within four months, i.e. June to September 2012, which were automatically observed by a wireless sensor network (WSN). Compared with MLP-MCA, the DBN-MCA model led to a decrease in root mean squared error (RMSE) by 18%. Thus, the differences of prediction errors increased due to the propagating errors of variables, difficulties of knowing soil properties and recording irrigation amount in practice. The sequential Gaussian simulation (sGs) was performed to assess the uncertainty of soil moisture estimations. Calculated with a threshold of SMC for each grid cell, the local uncertainty of simulated results in the post processing suggested that the probability of SMC less than 25% will be difference in different areas at different time periods. The current results showed that the DBN-MCA model performs better than the MLP-MCA model, and the DBN-MCA model provides a powerful tool for predicting SMC in highly non-linear forms. Moreover, because modeling soil moisture by using environmental variables is gaining increasing popularity, DBN techniques could contribute a lot to enhancing the calibration of MCA-based SMC estimations and hence provide an alternative approach for SMC monitoring in irrigation systems on the basis of canals.

Retrieval and Mapping of Soil Texture Based on Land Surface Diurnal Temperature Range Data from MODIS.

Numerous studies have investigated the direct retrieval of soil properties, including soil texture, using remotely sensed images. However, few have considered how soil properties influence dynamic changes in remote images or how soil processes affect the characteristics of the spectrum. This study investigated a new method for mapping regional soil texture based on the hypothesis that the rate of change of land surface temperature is related to soil texture, given the assumption of similar starting soil moisture conditions. The study area was a typical flat area in the Yangtze-Huai River Plain, East China. We used the widely available land surface temperature product of MODIS as the main data source. We analyzed the relationships between the content of different particle soil size fractions at the soil surface and land surface day temperature, night temperature and diurnal temperature range (DTR) during three selected time periods. These periods occurred after rainfalls and between the previous harvest and the subsequent autumn sowing in 2004, 2007 and 2008. Then, linear regression models were developed between the land surface DTR and sand (> 0.05 mm), clay (< 0.001 mm) and physical clay (< 0.01 mm) contents. The models for each day were used to estimate soil texture. The spatial distribution of soil texture from the studied area was mapped based on the model with the minimum RMSE. A validation dataset produced error estimates for the predicted maps of sand, clay and physical clay, expressed as RMSE of 10.69%, 4.57%, and 12.99%, respectively. The absolute error of the predictions is largely influenced by variations in land cover. Additionally, the maps produced by the models illustrate the natural spatial continuity of soil texture. This study demonstrates the potential for digitally mapping regional soil texture variations in flat areas using readily available MODIS data.

Predictive Mapping of Topsoil Organic Carbon in an Alpine Environment Aided by Landsat TM

The objective of this study was to examine the reflectance of Landsat TM imagery for mapping soil organic Carbon (SOC) content in an Alpine environment. The studied area (ca. 3*104 km2) is the upper reaches of the Heihe River at the northeast edge of the Tibetan plateau, China. A set (105) of topsoil samples were analyzed for SOC. Boosted regression tree (BRT) models using Landsat TM imagery were built to predict SOC content, alone or with topography and climate covariates (temperature and precipitation). The best model, combining all covariates, was only marginally better than using only imagery. Imagery alone was sufficient to build a reasonable model; this was a bit better than only using topography and climate covariates. The Lin’s concordance correlation coefficient values of the imagery only model and the full model are very close, larger than the topography and climate variables based model. In the full model, SOC was mainly explained by Landsat TM imagery (65% relative importance), followed by climate variables (20%) and topography (15% of relative importance). The good results from imagery are likely due to (1) the strong dependence of SOC on native vegetation intensity in this Alpine environment; (2) the strong correlation in this environment between imagery and environmental covariables, especially elevation (corresponding to temperature), precipitation, and slope aspect. We conclude that multispectral satellite data from Landsat TM images may be used to predict topsoil SOC with reasonable accuracy in Alpine regions, and perhaps other regions covered with natural vegetation, and that adding topography and climate covariables to the satellite data can improve the predictive accuracy.

First principles study of influence of alloying elements on TiAl: Lattice distortion

The influence of ternary additions Cr, Fe, Mn, Ni, Zr, Nb, Mo, Hf Ta, Si, Ga, Ge, In, and Sb, as well as the anti-site defects of both Ti and Al, on lattice parameters of TiAl were studied by the first principles electronic structure calculations with a discrete variational cluster method. The results of the calculation show that the effect of ternary additions on the distortion of TiAl lattice varies with the substitution behavior of the individual alloying element involved The. addition nf alloying elements in TiAl caused. a change in the electronic structure and the density of states of the system and results a change in the electronic structure and the density of states of the system and results in variation of the bond strength between the atoms. The total and partial density of states (DOS) of binary TiAl and of ternary TiAl-M, M = Cr, Zr, and Sb, etc., were comparatively examined. The relationship between the DOS and the bond strength is discussed. The present work suggests that the origin of the lattice distortion of the ternary TiAl-M systems lies in the variation of the electronic structure.

Pedogenetic evolution of clay minerals and agricultural implications in three paddy soil chronosequences of south China derived from different parent materials

PurposeThis study aims to understand how clay minerals change sequentially with paddy cultivation age and how parent materials (or original soils) affect the clay mineral behavior of paddy soils.Materials and methodsThree paddy soil chronosequences in the hilly regions of South China, derived from purple sandy shale (PS), Quaternary red clays (RC), and red sandstone (RS), were selected to explore the dynamic changes in clay mineralogy, by comparing physical, chemical, and mineralogical properties of soil sequences.Results and discussionFor RC and RS soils, both of which have a low K content, there was little change in the clay minerals. Long-term paddy cultivation can promote formation of illite-like minerals; however, this form of K storage was limited under present farming conditions. In PS soils, which are abundant in K-bearing minerals, the depotassication was strong, accompanied by marked transformation of clay minerals. Kaolinite-like minerals gradually decreased with paddy cultivation age; by contrast, derivative clay minerals such as secondary chlorite and halloysite gradually increased. Strong depotassication mainly occurred in the nonclay fractions. The rate of depotassication and the generation of clay fractions were much faster than in natural soils.ConclusionsThe clay minerals of paddy soils mainly followed the feature of their original soils. Their evolutions could be distinguished based on their constituents, which are greatly affected by their parent materials. Moreover, paddy cultivation is able to modify clay mineralogy, according to the original mineralogy and paddy soil management.

Selection of “Local” Models for Prediction of Soil Organic Matter Using a Regional Soil Vis-NIR Spectral Library

Abstract Soil spectral libraries have been established as a reference for predicting soil properties by visible and near-infrared (Vis-NIR) spectroscopy. Numerous studies show that predictions of soil properties over a local area can be improved by selecting an appropriate “local” subset from a large library; although these have usually been geographically local, they can be local in other than the geographic sense. We investigated prediction of soil organic matter at a local site using a regional soil Vis-NIR spectral library with 1,365 samples. Models built using the entire library were compared with subsets selected by (i) parent material (Cali_pm), (ii) land use type (Cali_lu), (iii) material-land use combination (Cali_com), and (iv) spectral similarity (Cali_ss). Models were built by partial least-squares regression, and their performances were evaluated using two independent test sets, one for paddy field (Test_paddy) and another one for upland agriculture (Test_up). Prediction accuracy was measured by the ratio of percentage deviation (RPD) compared with models built on the entire library. Ratios of percentage deviation for Cali_lu increased from 1.58 to 1.65 (Test_up) and from 2.05 to 3.02 (Test_paddy); for Cali_ss, RPD increased from 1.58 to 1.89 (Test_up) and from 2.05 to 2.26 (Test_paddy). Cali_pm models performed well for Test_paddy (RPD = 2.76) but poorly for Test_up (RPD = 1.11). Cali_com models used the fewest number of samples and performed poorly for both test sets (RPD < 1.5). These results show the potential of using land use types or spectral similarity to select “local” models for prediction of soil organic matter using a regional spectral library.