Prediction model for mercury transfer from soil to corn grain and its cross-species extrapolation

In this study the transfer characteristics of mercury（Hg） from a wide range of Chinese soils to corn grain（cultivar Zhengdan 958） were investigated. Prediction models were developed for determining the Hg bioconcentration factor（BCF） of Zhengdan 958 from soil, including the soil properties, such as p H, organic matter（OM） concentration, cation exchange capacity（CEC）, total nitrogen concentration（TN）, total phosphorus concentration（TP）, total potassium concentration（TK）, and total Hg concentration（THg）, using multiple stepwise regression analysis. These prediction models were applied to other non-model corn cultivars using a cross-species extrapolation approach. The results indicated that the soil p H was the most important factor associated with the transfer of Hg from soil to corn grain. Hg bioaccumulation in corn grain increased with the decreasing p H. No significant differences were found between two prediction models derived from different rates of Hg applied to the soil as HgCl2. The prediction models established in this study can be applied to other non-model corn cultivars and are useful for predicting Hg bioconcentration in corn grain and assessing the ecological risk of Hg in different soils.

Grain Yield Predict Based on GRA-AdaBoost-SVR Model

Grain yield security is a basic national policy of China,and changes in grain yield are influenced by a variety of factors,which often have a complex,non-linear relationship with each other.Therefore,this paper proposes a Grey Relational Analysis-Adaptive Boosting-Support Vector Regression(GRA-AdaBoost-SVR)model,which can ensure the prediction accuracy of the model under small sample,improve the generalization ability,and enhance the prediction accuracy.SVR allows mapping to high-dimensional spaces using kernel functions,good for solving nonlinear problems.Grain yield datasets generally have small sample sizes and many features,making SVR a promising application for grain yield datasets.However,the SVR algorithm’s own problems with the selection of parameters and kernel functions make the model less generalizable.Therefore,the Adaptive Boosting(AdaBoost)algorithm can be used.Using the SVR algorithm as a training method for base learners in the AdaBoost algorithm.Effectively address the generalization capability problem in SVR algorithms.In addition,to address the problem of sensitivity to anomalous samples in the AdaBoost algorithm,the GRA method is used to extract influence factors with higher correlation and reduce the number of anomalous samples.Finally,applying the GRA-AdaBoost-SVR model to grain yield forecasting in China.Experiments were conducted to verify the correctness of the model and to compare the effectiveness of several traditional models applied to the grain yield data.The results show that the GRA-AdaBoost-SVR algorithm improves the prediction accuracy,the model is smoother,and confirms that the model possesses better prediction performance and better generalization ability.

Input-Occupancy-Output Technique and Its Applications in China' s Grain Output Prediction and Township and Village Enterprises Study

Under the support of National Natural Science Foundation of China including international cooperative research project, key project and other project, professor Chen Xikang from Academy of Mathematics and Systems Science under the Chinese Academy of Sciences, together with his colleagues, put forward in-put-occupancy-output technique and then used it in national grain output prediction approach. The main achievements are as follows: