Soil enzymes activities and microbial biomass have an important influence on nutrient cycling. The spatial distribution of soil enzymes activities and microbial biomass were examined along a latitudinal gradient in fa...Soil enzymes activities and microbial biomass have an important influence on nutrient cycling. The spatial distribution of soil enzymes activities and microbial biomass were examined along a latitudinal gradient in farmlands of Songliao Plain, Northeast China to assess the impact of climatic changes along the latitudinal transect on nutrient cycling in agroecosystems. Top soils (0-20 cm depth) were sampled in fields at 7 locations from north (Hailun) to south (Dashiqiao) in the end of October 2005 after maize harvest. The contents of total C, N, and P, C/N, available N, and available P increased with the latitude. The activities of invertase and acid phosphatase, microbial biomass (MB) C and N, and MBC/MBN were significantly correlated with latitude (P < 0.05, r2 = 0.198, 0.635, 0.558, 0.211 and 0.317, respectively), that is, increasing with the latitude. Significant positive correlations (P < 0.05) were observed between invertase activity and the total N and available P, and between acid phosphatase activity and the total C, C/N, available N, total P and available P. The urease, acid phosphatase, and dehydrogenase activities were significantly correlated with the soil pH and electrical conductivity (EC) (P < 0.05). MBC and MBN were positively correlated with the total C, C/N, and available P (P < 0.05). The MBC/MBN ratio was positively correlated with the total C, total N, C/N, and available N (P < 0.05). The spatial distribution of soil enzyme activities and microbial biomass resulted from the changes in soil properties such as soil organic matter, soil pH, and EC, partially owing to variations in temperature and rainfall along the latitudinal gradient.展开更多
In the study, an improved approach was proposed to identify the contribution shares of three group factors that are climate, technology and input, social economic factors by which the grain production is shaped. In or...In the study, an improved approach was proposed to identify the contribution shares of three group factors that are climate, technology and input, social economic factors by which the grain production is shaped. In order to calibrate the method, Jiangxi Province, one of the main paddy rice producers in China was taken as an example. Based on 50 years （1961-2010） meteorological and statistic data, using GIS and statistical analysis tools, the three group factors that in certain extent impact China＇s paddy rice production have been analyzed quantitatively. The individual and interactive contribution shares of each factor group have been identiifed via eta square （η2）. In the paper, two group ordinary leasr square （OLS） models, paddy models and climate models, have been constructed for further analysis. Each model group consists of seven models, one full model and six partial models. The results of paddy models show that climate factors individually and interactively contribute 11.42-15.25%explanatory power to the variation of paddy rice production in the studied province. Technology and input factors contribute 16.17%individually and another 8.46%interactively together with climate factors, totally contributing about 25%. Social economic factors contribute about 7%of which 4.65%is individual contribution and 2.49%is interactive contribution together with climate factors. The three factor groups individually contribute about 23%and interactively contribute additional 41%to paddy rice production. In addition every two of the three factor groups also function interactively and contribute about 22%. Among the three factor groups, technology and input are the most important factors to paddy rice production. The results of climate models support the results of paddy models, and display that solar radiation （indicated by sunshine hour variable） is the dominate climate factor for paddy rice production.展开更多
基金the National Key Basic Research Support Foundation of China (No.2005CB121105)theNational Natural Science Foundation of China (No.30670379).
文摘Soil enzymes activities and microbial biomass have an important influence on nutrient cycling. The spatial distribution of soil enzymes activities and microbial biomass were examined along a latitudinal gradient in farmlands of Songliao Plain, Northeast China to assess the impact of climatic changes along the latitudinal transect on nutrient cycling in agroecosystems. Top soils (0-20 cm depth) were sampled in fields at 7 locations from north (Hailun) to south (Dashiqiao) in the end of October 2005 after maize harvest. The contents of total C, N, and P, C/N, available N, and available P increased with the latitude. The activities of invertase and acid phosphatase, microbial biomass (MB) C and N, and MBC/MBN were significantly correlated with latitude (P < 0.05, r2 = 0.198, 0.635, 0.558, 0.211 and 0.317, respectively), that is, increasing with the latitude. Significant positive correlations (P < 0.05) were observed between invertase activity and the total N and available P, and between acid phosphatase activity and the total C, C/N, available N, total P and available P. The urease, acid phosphatase, and dehydrogenase activities were significantly correlated with the soil pH and electrical conductivity (EC) (P < 0.05). MBC and MBN were positively correlated with the total C, C/N, and available P (P < 0.05). The MBC/MBN ratio was positively correlated with the total C, total N, C/N, and available N (P < 0.05). The spatial distribution of soil enzyme activities and microbial biomass resulted from the changes in soil properties such as soil organic matter, soil pH, and EC, partially owing to variations in temperature and rainfall along the latitudinal gradient.
基金financed by the National Basic Research Program of China(2010CB951502)
文摘In the study, an improved approach was proposed to identify the contribution shares of three group factors that are climate, technology and input, social economic factors by which the grain production is shaped. In order to calibrate the method, Jiangxi Province, one of the main paddy rice producers in China was taken as an example. Based on 50 years （1961-2010） meteorological and statistic data, using GIS and statistical analysis tools, the three group factors that in certain extent impact China＇s paddy rice production have been analyzed quantitatively. The individual and interactive contribution shares of each factor group have been identiifed via eta square （η2）. In the paper, two group ordinary leasr square （OLS） models, paddy models and climate models, have been constructed for further analysis. Each model group consists of seven models, one full model and six partial models. The results of paddy models show that climate factors individually and interactively contribute 11.42-15.25%explanatory power to the variation of paddy rice production in the studied province. Technology and input factors contribute 16.17%individually and another 8.46%interactively together with climate factors, totally contributing about 25%. Social economic factors contribute about 7%of which 4.65%is individual contribution and 2.49%is interactive contribution together with climate factors. The three factor groups individually contribute about 23%and interactively contribute additional 41%to paddy rice production. In addition every two of the three factor groups also function interactively and contribute about 22%. Among the three factor groups, technology and input are the most important factors to paddy rice production. The results of climate models support the results of paddy models, and display that solar radiation （indicated by sunshine hour variable） is the dominate climate factor for paddy rice production.
