Soils were collected from 2-year (2-y) and 3-year (3-y) old red-pine seedling plots in two tree nurseries, Hayward in the north and Wilson in the southwestern part of Wisconsin State respectively, and equilibrated wit...Soils were collected from 2-year (2-y) and 3-year (3-y) old red-pine seedling plots in two tree nurseries, Hayward in the north and Wilson in the southwestern part of Wisconsin State respectively, and equilibrated with 0.01 M Ca(NO3)2 for soil solution Zn and Mn (solu-Zn and Mn), and with 0.01 M Ca(NO3)2+0.005 M EDTA for soil adsorbed Zn and Mn (ad-Zn and Mn). Buffering capacity of soil Zn and Mn (b-Zn and Mn) was obtained from the ratio of ad-Zn and Mn to the solu-Zn and Mn. The concerned traces in pine seedling needles (ndls), stems(sts) and roots (rts) were simultaneously measured. The results obtained show that:About 60% of solu- and ad- Zn ranged from 0.2 to 0.4 and from 1 to 2μ/g soil respectively. About 70% of b-Zn was within) 3-10.The highest content of solu-Zn compared with the lowest showed a discrepancy of more than 10-fold. The two forms of soil Zn were commonly higher in Wilson than in Hayward Nursery.About 80% of solu-, ad- and b-Mn were within 3-10, 5-5.8 μg/ g soil and 1-2 respectively. Influence of low buffering capacity on solu-Zn and Mn was about 20 times stronger than that of the high.The E-value, a ratio of accumulated Zn and Mn in needles to those in the soil solution, is proved to be: E-Zn > E-Mn;E-sts> E-ndls or E-rts; and E-2y > E-3y.Curvilinear and/ or linear correlations between soil solu-, ad- and b-Zn and Mn and ndls-, sts-, rts-Zn and Mn were at very significant or significant levels.For predicting ndls-Zn and Mn, two realizable and simple models from two regression equations were established through the selection of related parameters and dependent variables. Binary regression analysis basically eliminated the influence of soil pH on the prediction of Zn and Mn in needles. Soil pH was thus thought to be excluded from the model.展开更多
Among spatial interpolation techniques,geostatistics is generally preferred because it takes into account the spatial correlation between neighbouring observations in order to predict attribute values at unsampled loc...Among spatial interpolation techniques,geostatistics is generally preferred because it takes into account the spatial correlation between neighbouring observations in order to predict attribute values at unsampled locations.A doline of approximately 15 000 m 2 at 1 900 m above sea level (North Italy) was selected as the study area to estimate a digital elevation model (DEM) using geostatistics,to provide a realistic distribution of the errors and to demonstrate whether using widely available secondary data provided more accurate estimates of soil pH than those obtained by univariate kriging.Elevation was measured at 467 randomly distributed points that were converted into a regular DEM using ordinary kriging.Further,110 pits were located using spatial simulated annealing (SSA) method.The interpolation techniques were multi-linear regression analysis (MLR),ordinary kriging (OK),regression kriging (RK),kriging with external drift (KED) and multi-collocated ordinary cokriging (CKmc).A cross-validation test was used to assess the prediction performances of the different algorithms and then evaluate which methods performed best.RK and KED yielded better results than the more complex CKmc and OK.The choice of the most appropriate interpolation method accounting for redundant auxiliary information was strongly conditioned by site specific situations.展开更多
文摘Soils were collected from 2-year (2-y) and 3-year (3-y) old red-pine seedling plots in two tree nurseries, Hayward in the north and Wilson in the southwestern part of Wisconsin State respectively, and equilibrated with 0.01 M Ca(NO3)2 for soil solution Zn and Mn (solu-Zn and Mn), and with 0.01 M Ca(NO3)2+0.005 M EDTA for soil adsorbed Zn and Mn (ad-Zn and Mn). Buffering capacity of soil Zn and Mn (b-Zn and Mn) was obtained from the ratio of ad-Zn and Mn to the solu-Zn and Mn. The concerned traces in pine seedling needles (ndls), stems(sts) and roots (rts) were simultaneously measured. The results obtained show that:About 60% of solu- and ad- Zn ranged from 0.2 to 0.4 and from 1 to 2μ/g soil respectively. About 70% of b-Zn was within) 3-10.The highest content of solu-Zn compared with the lowest showed a discrepancy of more than 10-fold. The two forms of soil Zn were commonly higher in Wilson than in Hayward Nursery.About 80% of solu-, ad- and b-Mn were within 3-10, 5-5.8 μg/ g soil and 1-2 respectively. Influence of low buffering capacity on solu-Zn and Mn was about 20 times stronger than that of the high.The E-value, a ratio of accumulated Zn and Mn in needles to those in the soil solution, is proved to be: E-Zn > E-Mn;E-sts> E-ndls or E-rts; and E-2y > E-3y.Curvilinear and/ or linear correlations between soil solu-, ad- and b-Zn and Mn and ndls-, sts-, rts-Zn and Mn were at very significant or significant levels.For predicting ndls-Zn and Mn, two realizable and simple models from two regression equations were established through the selection of related parameters and dependent variables. Binary regression analysis basically eliminated the influence of soil pH on the prediction of Zn and Mn in needles. Soil pH was thus thought to be excluded from the model.
文摘Among spatial interpolation techniques,geostatistics is generally preferred because it takes into account the spatial correlation between neighbouring observations in order to predict attribute values at unsampled locations.A doline of approximately 15 000 m 2 at 1 900 m above sea level (North Italy) was selected as the study area to estimate a digital elevation model (DEM) using geostatistics,to provide a realistic distribution of the errors and to demonstrate whether using widely available secondary data provided more accurate estimates of soil pH than those obtained by univariate kriging.Elevation was measured at 467 randomly distributed points that were converted into a regular DEM using ordinary kriging.Further,110 pits were located using spatial simulated annealing (SSA) method.The interpolation techniques were multi-linear regression analysis (MLR),ordinary kriging (OK),regression kriging (RK),kriging with external drift (KED) and multi-collocated ordinary cokriging (CKmc).A cross-validation test was used to assess the prediction performances of the different algorithms and then evaluate which methods performed best.RK and KED yielded better results than the more complex CKmc and OK.The choice of the most appropriate interpolation method accounting for redundant auxiliary information was strongly conditioned by site specific situations.
