Showing 2 results for A. Jafari
A. Jafari Malekabadi, M. Afyuni, S. F. Mousavi, A. Khosravi,
Volume 8, Issue 3 (fall 2004)
Abstract
In recent decades, the use of nitrogen fertilizers has increased irrespective of their effects on soil properties, agricultural products and, particularly, on environmental pollution. Nitrate easily leaches from soils into groundwater. The objective of this study was to determine temporal and spatial nitrate concentrations in groundwater in agricultural, industrial and urban regions in some parts of Isfahan Province. Water samples were collected monthly from 75 agricultural, industrial, and urban wells of Isfahan, Najaf-abad, Shahreza, Natanz and Kashan during January-May 2001. The results indicated that NO3-N concentrations in most of the regions studied were higher than the standard level (10 mg/l) and nitrate pollution must be reckoned among the most serious problems of sustainable agriculture and exploitation of groundwater resources. Average NO3-N concentration in different wells ranged from 1.03 to 50.78 mg/l (4.64 to 228.5 mg/l as nitrate). The average NO3-N concentration in groundwater of Najaf-abad, Shahreza, Isfahan and Natanz-Kashan was 17.56, 14.6, 16.04, and 8.24 mg/l and 95.5, 100, 84 and 33.3 % of total wells in these regions had nitrate concentrations above the standard level, respectively. Maximum NO3-N concentration was detected in the agricultural region south of Najaf-abad (64.6 mg/l). Nitrate pollution in most of the sampling areas was mainly linked to agricultural activities. The average NO3-N concentration in groundwater of all agricultural, industrial, and urban regions, except for urban regions of Natanz and Kashan, were above the standard level. Generally, nitrate concentration level in groundwater increased with time and was maximum in March and April.
A. Jafari, H. Khademi, Sh. Ayoubi,
Volume 16, Issue 62 (Winte - 2013 2013)
Abstract
Digital soil mapping includes soils, spatial prediction and their properties based on the relationship with covariates. This study was designed for digital soil mapping using binary logistic regression and boosted regression tree in Zarand region of Kerman. A stratified sampling scheme was adopted for the 90,000 ha area based on which, 123 soil profiles were described. In both approaches, the occurrence of relevant diagnostic horizons was first mapped, and subsequently, various maps were combined for a pixel-wise classification by combining the presence or absence of diagnostic horizons. Covariates included a geomorphology map, terrain attributes and remote sensing indices. Among the predictors, geomorphology map was identified as an important tool for digital soil mapping approaches as it helped increase the prediction accuracy. After geomorphic surfaces, the terrain attributes were identified as the most effective auxiliary parameters in predicting the diagnostic horizons. The methods predicted high probability of salic horizon in playa landform, gypsic horizon in gypsiferous hills and calcic horizon in alluvial fans. Both models predicted Calcigypsids with very low reliability and accuracy, while prediction of Haplosalids and Haplogypsids was carried out with high accuracy.
