The accurate prediction of soybean yield is of great significance for agricultural production, monitoring and early warning.Although previous studies have used machine learning algorithms to predict soybean yield base...The accurate prediction of soybean yield is of great significance for agricultural production, monitoring and early warning.Although previous studies have used machine learning algorithms to predict soybean yield based on meteorological data,it is not clear how different models can be used to effectively separate soybean meteorological yield from soybean yield in various regions. In addition, comprehensively integrating the advantages of various machine learning algorithms to improve the prediction accuracy through ensemble learning algorithms has not been studied in depth. This study used and analyzed various daily meteorological data and soybean yield data from 173 county-level administrative regions and meteorological stations in two principal soybean planting areas in China(Northeast China and the Huang–Huai region), covering 34 years.Three effective machine learning algorithms(K-nearest neighbor, random forest, and support vector regression) were adopted as the base-models to establish a high-precision and highly-reliable soybean meteorological yield prediction model based on the stacking ensemble learning framework. The model's generalizability was further improved through 5-fold crossvalidation, and the model was optimized by principal component analysis and hyperparametric optimization. The accuracy of the model was evaluated by using the five-year sliding prediction and four regression indicators of the 173 counties, which showed that the stacking model has higher accuracy and stronger robustness. The 5-year sliding estimations of soybean yield based on the stacking model in 173 counties showed that the prediction effect can reflect the spatiotemporal distribution of soybean yield in detail, and the mean absolute percentage error(MAPE) was less than 5%. The stacking prediction model of soybean meteorological yield provides a new approach for accurately predicting soybean yield.展开更多
The simultaneous nitrification and denitrification in step-feeding biological nitrogen removal process were investigated under different influent substrate concentrations and aeration flow rates. Biological occurrence...The simultaneous nitrification and denitrification in step-feeding biological nitrogen removal process were investigated under different influent substrate concentrations and aeration flow rates. Biological occurrence of simultaneous nitrification and denitrification was verified in the aspect of nitrogen mass balance and alkalinity. The experimental results also showed that there was a distinct linear relationship between simultaneous nitrification and denitrification and DO concentration under the conditions of low and high aeration flow rate. In each experimental run the floc sizes of activated sludge were also measured and the results showed that simultaneous nitrification and denitrification could occur with very small size of floc.展开更多
选择以西天山野果林为例,应用徕卡TS06无合作目标性全站仪(Reflectorless Total Station)获取野苹果林树台的形态参数数据,定量分析西天山野苹果林树台的三维形态特征和空间分布格局,构建野苹果林树台的DEM(数字高程模型Digital Elevati...选择以西天山野果林为例,应用徕卡TS06无合作目标性全站仪(Reflectorless Total Station)获取野苹果林树台的形态参数数据,定量分析西天山野苹果林树台的三维形态特征和空间分布格局,构建野苹果林树台的DEM(数字高程模型Digital Elevation Model,DEM),对野苹果林树台微地貌景观状况进行研究。结果表明:(1)研究区的野苹果树台的形态比较规则,长宽比为0.657 9～1.431 0,差异比较小,呈扇形或半圆形。且能够明显看出野苹果树所在的位置(即样地中心)颜色较深,且突出,高程值较大。(2)坡度、底径与野苹果林树台的底面积、表面积、体积、长、宽、高、长宽比都呈现出显著相关性。其中,野苹果林树台的坡度与树台的高和长度之间呈现出明显的线性正相关性,R^2分别为0.96、0.90;树台的底径与其底面积、表面积、长度呈现出较明显的线性相关,R^2分别为0.61、0.46、0.47。其研究结果可为合理利用森林植被,水土保持,以及野苹果树根部营养储存量提供一定的理论支持。展开更多
The primary goal of Chinese agricultural development is to guarantee national food security and supply of major agricultural products. Hence, the scientiifc work on agricultural monitoring and early warning as wel as ...The primary goal of Chinese agricultural development is to guarantee national food security and supply of major agricultural products. Hence, the scientiifc work on agricultural monitoring and early warning as wel as agricultural outlook must be strengthened. In this study, we develop the China Agricultural Monitoring and Early-warning System (CAMES) on the basis of a comparative study of domestic and international agricultural outlook models. The system is a dynamic and multi-market partial equilibrium model that integrates biological mechanisms with economic mechanisms. This system, which includes 11 categories of 953 kinds of agricultural products, could dynamical y project agricultural market supply and demand, assess food security, and conduct scenario analysis at different spatial levels, time scale levels, and macro-micro levels. Based on the CAMES, the production, consumption, and trade of the major agricultural products in China over the next decade are projected. The fol owing conclusions are drawn:i) The production of major agricultural products wil continue to grow steadily, mainly because of the increase in yield. i ) The growth of agricultural consumption wil be slightly higher than that of agricultural production. Meanwhile, a high self-sufifciency rate is expected for cereals such as rice, wheat, and maize, with the rate being stable at around 97%. i i) Agricultural trade wil continue to thrive. The growth of soybean and milk im-ports wil slow down, but the growth of traditional agricultural exports such as vegetables and fruits is expected to continue.展开更多
The spatial-temporal patterns of grain production and consumption have an important influence on the effective national grain supply on condition of tight balance in the total grain amount in China. In this paper, we ...The spatial-temporal patterns of grain production and consumption have an important influence on the effective national grain supply on condition of tight balance in the total grain amount in China. In this paper, we analyze the spatial-temporal pattems of grain production, consumption and the driving mechanism for their evolution processes in China. The results indicate that both gravity centers of grain production and consumption in China moved toward the northern and eastern regions, almost in the same direction. The coordination of grain production and consumption increased slightly from 1995 to 2007 but decreased from 2000 to 2007. There is a spatial difference between the major districts of output increase and the strong growth potential in grain consumption, which indicates an increasing difficulty in improving the regional coordination of grain production and consumption. The movement of the gravity center of grain production is significantly correlated with regional differences in grain production policy, different economic development models, and spatial disparity of land and water resource use. For grain consumption, the main driving factors include rapid urbanization, the upgrade of food consumption structure, and distribution of food industries.展开更多
基金supported by the Science and Technology Innovation Project of Chinese Academy of Agricultural Sciences (CAAS-ASTIP-2016-AII)。
文摘The accurate prediction of soybean yield is of great significance for agricultural production, monitoring and early warning.Although previous studies have used machine learning algorithms to predict soybean yield based on meteorological data,it is not clear how different models can be used to effectively separate soybean meteorological yield from soybean yield in various regions. In addition, comprehensively integrating the advantages of various machine learning algorithms to improve the prediction accuracy through ensemble learning algorithms has not been studied in depth. This study used and analyzed various daily meteorological data and soybean yield data from 173 county-level administrative regions and meteorological stations in two principal soybean planting areas in China(Northeast China and the Huang–Huai region), covering 34 years.Three effective machine learning algorithms(K-nearest neighbor, random forest, and support vector regression) were adopted as the base-models to establish a high-precision and highly-reliable soybean meteorological yield prediction model based on the stacking ensemble learning framework. The model's generalizability was further improved through 5-fold crossvalidation, and the model was optimized by principal component analysis and hyperparametric optimization. The accuracy of the model was evaluated by using the five-year sliding prediction and four regression indicators of the 173 counties, which showed that the stacking model has higher accuracy and stronger robustness. The 5-year sliding estimations of soybean yield based on the stacking model in 173 counties showed that the prediction effect can reflect the spatiotemporal distribution of soybean yield in detail, and the mean absolute percentage error(MAPE) was less than 5%. The stacking prediction model of soybean meteorological yield provides a new approach for accurately predicting soybean yield.
基金Project supported by the Key International Cooperative Program of NSFC(No. 50521140075)the Hi-Tech Research and Development Program(863)of China(No. 2004AA601020)the Attached Projects of"863"Project of Beijing Municipal Science and Technology(No.20005186040421).
文摘The simultaneous nitrification and denitrification in step-feeding biological nitrogen removal process were investigated under different influent substrate concentrations and aeration flow rates. Biological occurrence of simultaneous nitrification and denitrification was verified in the aspect of nitrogen mass balance and alkalinity. The experimental results also showed that there was a distinct linear relationship between simultaneous nitrification and denitrification and DO concentration under the conditions of low and high aeration flow rate. In each experimental run the floc sizes of activated sludge were also measured and the results showed that simultaneous nitrification and denitrification could occur with very small size of floc.
基金supported by the National Natural Science Foundation of China (71303238)the National Science and Technology Support Plan Projects (2012BAH20B04)the compilation group of the China Agricultural Outlook Report (2015–2024)
文摘The primary goal of Chinese agricultural development is to guarantee national food security and supply of major agricultural products. Hence, the scientiifc work on agricultural monitoring and early warning as wel as agricultural outlook must be strengthened. In this study, we develop the China Agricultural Monitoring and Early-warning System (CAMES) on the basis of a comparative study of domestic and international agricultural outlook models. The system is a dynamic and multi-market partial equilibrium model that integrates biological mechanisms with economic mechanisms. This system, which includes 11 categories of 953 kinds of agricultural products, could dynamical y project agricultural market supply and demand, assess food security, and conduct scenario analysis at different spatial levels, time scale levels, and macro-micro levels. Based on the CAMES, the production, consumption, and trade of the major agricultural products in China over the next decade are projected. The fol owing conclusions are drawn:i) The production of major agricultural products wil continue to grow steadily, mainly because of the increase in yield. i ) The growth of agricultural consumption wil be slightly higher than that of agricultural production. Meanwhile, a high self-sufifciency rate is expected for cereals such as rice, wheat, and maize, with the rate being stable at around 97%. i i) Agricultural trade wil continue to thrive. The growth of soybean and milk im-ports wil slow down, but the growth of traditional agricultural exports such as vegetables and fruits is expected to continue.
基金supported by the Key Technologies R&D Program of China during the 12th Five-Year Plan period(2012BAH20B04)the National Natural Science Foundation of China(41201599, 41001108)+1 种基金the Natural Science Foundation of Shandong Province,China(ZR2009DL011)the Social Science Foundation of China(08BJY113)
文摘The spatial-temporal patterns of grain production and consumption have an important influence on the effective national grain supply on condition of tight balance in the total grain amount in China. In this paper, we analyze the spatial-temporal pattems of grain production, consumption and the driving mechanism for their evolution processes in China. The results indicate that both gravity centers of grain production and consumption in China moved toward the northern and eastern regions, almost in the same direction. The coordination of grain production and consumption increased slightly from 1995 to 2007 but decreased from 2000 to 2007. There is a spatial difference between the major districts of output increase and the strong growth potential in grain consumption, which indicates an increasing difficulty in improving the regional coordination of grain production and consumption. The movement of the gravity center of grain production is significantly correlated with regional differences in grain production policy, different economic development models, and spatial disparity of land and water resource use. For grain consumption, the main driving factors include rapid urbanization, the upgrade of food consumption structure, and distribution of food industries.
