利用人工神经网络以及相关地形属性绘制数字土壤地图

Detailed soil surveys involve costly and time-consuming work and require expert knowledge. Since soil surveys provide information to meet a wide range of needs, new methods are necessary to map soils quickly and accurately. In this study, multilayer perceptron artificial neural networks (ANNs) were developed to map soil units using digital elevation model (DEM) attributes. Several optimal ANNs were produced based on a number of input data and hidden units. The approach used test and validation areas to calculate the accuracy of interpolated and extrapolated data. The results showed that the system and level of soil classification employed had a direct effect on the accuracy of the results. At the lowest level, smaller errors were observed with the World Reference Base (WRB) classification criteria than the Soil Taxonomy (ST) system, but more soil classes could be predicted when using ST (7 soils in the case of ST vs. 5 with WRB). Training errors were below 11% for all the ANN models applied, while the test error (interpolation error) and validation error (extrapolation error) were as high as 50% and 70%, respectively. As expected, soil prediction using a higher level of classification presented a better overall level of accuracy. To obtain better predictions, in addition to DEM attributes, data related to landforms and/or lithology as soil-forming factors, should be used as ANN input data.     

Characterizing Spatial Variability of Soil Textural Fractions and Fractal Parameters Derived from Particle Size Distributions

Soil particle size distribution (PSD) is a fundamental physical property affecting other soil properties. Characterizing spatial variability of soil texture is very important in environmental research. The objectives of this work were: 1) to partition PSD of 75 soil samples, collected from a flat field in the University of Guilan, Iran, into two scaling domains using a piecewise fractal model to evaluate the relationships between fractal dimensions of scaling domains and soil clay, silt, and sand fractions and 2) to assess the potential of fractal parameters as an index used in a geostatistical approach reflecting the spatial variability of soil texture. Features of PSD of soil samples were studied using fractal geometry, and geostatistical techniques were used to characterize the spatial variability of fractal and soil textural parameters. There were two scaling domains for the PSD of soil samples. The fractal dimensions of these two scaling domains (D₁ and D₂) were then used to characterize different ranges of soil particle sizes and their relationships to the soil textural parameters. There was a positive correlation between D₁ and clay content (R² = 0.924), a negative correlation between D₁ and silt content (R² = 0.801), and a negative correlation between D₂ and sand content (R² = 0.913). The geometric mean diameter of soil particles had a negative correlation with D₁ (R² = 0.569) and D₂ (R² = 0.682). Semivariograms of fractal dimensions and soil textural parameters were calculated and the maps of spatial variation of D₁ and D₂ and soil PSD parameters were provided using ordinary kriging. The results showed that there were also spatial correlations between D₁ and D₂ and particle size fractions. According to the semivariogram models and validation parameters, the fractal parameters had powerful spatial structure and could better describe the spatial variability of soil texture.     

Effect of age,sex, and season on the prevalence of Linguatula serrata infestation in mesenteric lymph nodes of goats slaughtered in Tabriz,Iran

Tropical Animal Health and Production - This research was conducted to determine the prevalence and intensity of infection with nymphs of Linguatula serrata in mesenteric lymph nodes of the goats...     

基于人工神经网络的数据处理和多目标群方法用土壤传递函数(PTFs)估算保水性

Pedotransfer functions(PTFs) have been developed to estimate soil water retention curves(SWRC) by various techniques.In this study PTFs were developed to estimate the parameters(θ s,θ r,α and λ) of the Brooks and Corey model from a data set of 148 samples.Particle and aggregate size distribution fractal parameters(PSDFPs and ASDFPs,respectively) were computed from three fractal models for either particle or aggregate size distribution.The most effective model in each group was determined by sensitivity analysis.Along with the other variables,the selected fractal parameters were employed to estimate SWRC using multi-objective group method of data handling(mGMDH) and different topologies of artificial neural networks(ANNs).The architecture of ANNs for parametric PTFs was different regarding the type of ANN,output layer transfer functions and the number of hidden neurons.Each parameter was estimated using four PTFs by the hierarchical entering of input variables in the PTFs.The inclusion of PSDFPs in the list of inputs improved the accuracy and reliability of parametric PTFs with the exception of θ s.The textural fraction variables in PTF1 for the estimation of α were replaced with PSDFPs in PTF3.The use of ASDFPs as inputs significantly improved α estimates in the model.This result highlights the importance of ASDFPs in developing parametric PTFs.The mGMDH technique performed significantly better than ANNs in most PTFs.     

The effect of magnesium sulfate on bleeding time and nitric oxide production in preeclamsia

Preeclampsia is a disease regarding with altered vascular reactivity leading to hypertension of the mother and metabolic alterations in the fetus. This study aimed to assess nitric oxide and bleeding time following administration of magnesium sulfate to preeclamtic patients compared to normotensive pregnant women. A total of 112 subjects (56 preeclamtic patients and 56 normotensive pregnant controls) were enrolled in this case-control study. Cases and controls were matched for age, BMI, gestational age, parity and gravidity. Total concentration of nitrite and nitrate (NOx) was measured before and during magnesium sulfate (MgSO4) treatment using a modified Griess-based method. RESULTS: Systolic and diastolic blood pressures were significantly decreased during MgSO4 treatment in preeclamtic patients (p < 0.0001). NOx levels were significantly increased in preeclamtic women after MgSO4 administration (33.7 +/- 18.5 vs. 50.2 +/- 21.6, p < 0.0001) but it was not seen in normotensive parturients (52.4 +/- 28.9 vs. 57.3 +/- 21.7, p = 0.362). The bleeding time was scarcely increased following magnesium sulfate treatment in preeclamptic patients compared to normotensive pregnant women but it was not significant (p = 0.18). In addition, there was only a significantly reverse correlation between NOx levels and systolic or diastolic blood pressure in preeclamtic parturients after MgSO4 treatment (r = -0384; p = 0.003 and r = -0.29; p = 0.03, respectively). This study demonstrates that administrating MgSO4 to preeclamtic patients induced significant changes in NOx production which had a major role in modulating vasculature changes in preeclamsia.     

基于地形属性使用回归, 克里格和人工神经网络方法估算饱和导水率空间分布

Several methods,including stepwise regression,ordinary kriging,cokriging,kriging with external drift,kriging with varying local means,regression-kriging,ordinary artificial neural networks,and kriging combined with artificial neural networks,were compared to predict spatial variation of saturated hydraulic conductivity from environmental covariates.All methods except ordinary kriging allow for inclusion of secondary variables.The secondary spatial information used was terrain attributes including elevation,slope gradient,slope aspect,profile curvature and contour curvature.A multiple jackknifing procedure was used as a validation method.Root mean square error (RMSE) and mean absolute error (MAE) were used as the validation indices,with the mean RMSE and mean MAE used to judge the prediction quality.Prediction performance by ordinary kriging was poor,indicating that prediction of saturated hydraulic conductivity can be improved by incorporating ancillary data such as terrain variables.Kriging combined with artificial neural networks performed best.These prediction models made better use of ancillary information in predicting saturated hydraulic conductivity compared with the competing models.The combination of geostatistical predictors with neural computing techniques offers more capability for incorporating ancillary information in predictive soil mapping.There is great potential for further research and development of hybrid methods for digital soil mapping.     

Association of Mycobacterium avium subspecies paratuberculosis infection with milk production and calving interval in Iranian Holsteins

There are inconsistent results for the association of Mycobacterium avium subspecies paratuberculosis (MAP) infection with production and reproduction in dairy cows. Determination of these associations in each region is essential to encourage participation of dairy cattle producers in disease control programs. This study was conducted in Shiraz, southern Iran, to quantify the association of subclinical MAP infection with 305-day milk production and calving interval in Iranian Holsteins. A total of 21 dairy herds were selected for the study and in each herd, quarter milk samples were collected from ten to 12 dairy cows for PCR analysis. Data about parity, calving interval, length of lactation period, total milk production and 305-day milk production were also provided for each animal. Overall, 252 individual milk samples were collected. Herd- and individual-level prevalence of MAP infection were 23.8% (95% CI, 6.2–41.4%) and 3.2% (95% CI, 1.3–5.1%), respectively based on IS900 nested PCR. The results for 305-day milk production revealed a 248 kg reduction in positive cows compared with negative ones (P = 0.009). When cows from positive herds were compared with cows from negative herds, a 335-kg reduction in 305-day milk production (P = 0.005) and a 30-day increase in calving interval (P = 0.057) were observed in the former group. These findings support the previous results that paratuberculosis infection is negatively associated with the performance of the animals.     

Weed control in irrigated corn by hairy vetch interseeded at different rates and times

In order to study the potential of interseeded hairy vetch as a living mulch to control weeds in corn, a field study was conducted at the Agricultural Research Farm, Razi University, Kermanshah, Iran. The experiment was carried out in a split-split plot arrangement with four replications. The main plots were two weed treatments (weedy and weed-free for all of the growing season), the subplots were two hairy vetch planting dates (simultaneous with corn planting and 10 days after corn emergence), and the sub-subplots were three hairy vetch planting rates (0, 25, and 50 kg ha⁻¹). The results indicated that the weedy condition for all of the growing season reduced corn plant traits, including the seed yield, number of ears per plant, number of seeds per ear, 100-seed weight, height, Leaf Area Index, and leaf chlorophyll content, as compared to the weed-free condition for the entire growing season. The hairy vetch dry weight also was reduced by the full-season weedy condition. The traits under study were not significantly influenced by the hairy vetch planting times, but increasing the hairy vetch planting rate from 0 to 50 kg ha⁻¹ improved the corn yield, number of seeds per ear, 100-seed weight, height, Leaf Area Index, and leaf chlorophyll content. However, the number of ears per plant was not significantly influenced and the weed dry weight was reduced by half. Overall, from the standpoint of corn seed production, an interseeding amount of 25 kg ha⁻¹ of hairy vetch is recommended; taking into consideration the legume forage yield, an interseeding amount of 50 kg ha⁻¹ of hairy vetch is more beneficial.     

Effects of Haloxylon spp. of Different Age Classes on Vegetation Cover and Soil Properties on an Arid Desert Steppe in Iran

In arid regions, afforestation has been considered as a method for ecological revival in terms of vegetation enrichment and soil amelioration. In this study, the effects of afforestation with Haloxylon spp. on vegetation cover and soil properties were measured < 3, 3, 6, and 25 years after planting in an arid desert in Iran. Soil samples were collected at two depths (0-30 and 30-60 cm) under and between shrub canopies. Afforestation succession significantly affected plant community characteristics. In total, 16 species from 8 families and 15 genera were observed along the afforestation successional gradient. Plant species richness and diversity and vegetation cover increased slowly during the succession, and reached the maximum values in the area where Haloxylon had been planted for 25 years. Soil nutrient values gradually increased during the succession, and the levels of organic matter, total nitrogen, available potassium, and available phosphorus were significantly higher under Haloxylon canopies than between them. Afforestation reduced soil pH under and between Haloxylon canopies during the succession, while soil electrical conductivity followed an opposite pattern. Haloxylon planting increased the silt content in the 0-30 cm soil layer. Our results suggest that Haloxylon establishment plays an important role in the reestablishment of desertified ecosystems in arid regions.