Changes in groundwater level in Homand-Absard plain, located in north-west of Kavir-e-Markazi watershed and east of Tehran province, were studied. The used research method was descriptive approach, and the research st...Changes in groundwater level in Homand-Absard plain, located in north-west of Kavir-e-Markazi watershed and east of Tehran province, were studied. The used research method was descriptive approach, and the research study was conducted based on field and desk surveys. The data needed was provided from field surveys, contours maps, and data of observation wells. There were 17 observation wells in the study area where the changes in groundwater levels were measured during 1996-2013, and an index hydrograph was prepared for the aquifer of plain. The sharpest decline in the groundwater level was in the central of Homan-Absard plain. There was 1.43 m decline in the groundwater level of aquifer annually, compared with similar studies in other parts of Iran, it has a high rate, and to the average, the groundwater level of plain has dropped equal to 25.76 m, 1996-2013. According to the study findings, the groundwater level changes with those in rainfall weren't match and the drop in groundwater level during wet years and then has continued which represents the high water extraction factor on the groundwater level drop.展开更多
Nitrogen （N） was applied at rates of 0, 100, 200 and 300 kg.ha^-1 and boron （B） was applied as foliar at rates 0, 500 and 1000 g.ha^-1 to study the effect of different application rates of nitrogen and boron ferti...Nitrogen （N） was applied at rates of 0, 100, 200 and 300 kg.ha^-1 and boron （B） was applied as foliar at rates 0, 500 and 1000 g.ha^-1 to study the effect of different application rates of nitrogen and boron fertilizers on yield, yield components and fiber properties of cotton. Statistical results of study showed that N application significantly （P 〈 0.05） enhanced boll number, boll weight, seed cotton weight of boll, seed cotton yield and lint yield. Results of study also indicated that the maximum seed cotton yield was recorded in case of 200 kg.ha^-1 N application rate, and this application rate resulted in 19.6% increased seed cotton yield. Statistical results also indicated that foliar application of B significantly enhanced boll number, boll weight, seed cotton yield and lint yield. Results also demonstrated that the maximum seed cotton yield was obtained in case of 1000 g.hal foliar application of B, and this foliar application rate resulted in 25% increased seed cotton yield. Statistical results showed that effect of different application rates of N was not significant for all fiber properties （fiber length, fiber strength and fiber fineness）. Conversely, results of study indicated that different application rates of B significantly affected some fiber properties.展开更多
Environmental impact prediction is an important step in many environmental studies. A wide variety of methods have been developed in this concern. During this study, remote sensing images were used for environmental i...Environmental impact prediction is an important step in many environmental studies. A wide variety of methods have been developed in this concern. During this study, remote sensing images were used for environmental impact prediction in Ro-batkarim area, Iran, during the years of 2005～2007. It was assumed that environmental impact could be predicted using time series satellite imageries. Natural vegetation cover was chosen as a main environmental element and a case study. Environmental impacts of the regional development on natural vegetation of the area were investigated considering the changes occurred on the extent of natural vegetation cover and the amount of biomass. Vegetation data, land use and land cover classes (as activity factors) within several years were prepared using satellite images. The amount of biomass was measured by Soil-adjusted Vegetation Index (SAVI) and Normalized Difference Vegetation Index (NDVI) based on satellite images. The resulted biomass estimates were tested by the paired samples t-test method. No significant difference was observed between the average biomass of estimated and control samples at the 5% significance level. Finally, regression models were used for the environmental impacts prediction. All obtained regression models for prediction of impacts on natural vegetation cover show values over 0.9 for both correlation coefficient and R-squared. According to the resulted methodology, the prediction models of projects and plans impacts can also be developed for other environmental elements which may be derived using time series remote sensing images.展开更多
文摘Changes in groundwater level in Homand-Absard plain, located in north-west of Kavir-e-Markazi watershed and east of Tehran province, were studied. The used research method was descriptive approach, and the research study was conducted based on field and desk surveys. The data needed was provided from field surveys, contours maps, and data of observation wells. There were 17 observation wells in the study area where the changes in groundwater levels were measured during 1996-2013, and an index hydrograph was prepared for the aquifer of plain. The sharpest decline in the groundwater level was in the central of Homan-Absard plain. There was 1.43 m decline in the groundwater level of aquifer annually, compared with similar studies in other parts of Iran, it has a high rate, and to the average, the groundwater level of plain has dropped equal to 25.76 m, 1996-2013. According to the study findings, the groundwater level changes with those in rainfall weren't match and the drop in groundwater level during wet years and then has continued which represents the high water extraction factor on the groundwater level drop.
文摘Nitrogen （N） was applied at rates of 0, 100, 200 and 300 kg.ha^-1 and boron （B） was applied as foliar at rates 0, 500 and 1000 g.ha^-1 to study the effect of different application rates of nitrogen and boron fertilizers on yield, yield components and fiber properties of cotton. Statistical results of study showed that N application significantly （P 〈 0.05） enhanced boll number, boll weight, seed cotton weight of boll, seed cotton yield and lint yield. Results of study also indicated that the maximum seed cotton yield was recorded in case of 200 kg.ha^-1 N application rate, and this application rate resulted in 19.6% increased seed cotton yield. Statistical results also indicated that foliar application of B significantly enhanced boll number, boll weight, seed cotton yield and lint yield. Results also demonstrated that the maximum seed cotton yield was obtained in case of 1000 g.hal foliar application of B, and this foliar application rate resulted in 25% increased seed cotton yield. Statistical results showed that effect of different application rates of N was not significant for all fiber properties （fiber length, fiber strength and fiber fineness）. Conversely, results of study indicated that different application rates of B significantly affected some fiber properties.
文摘Environmental impact prediction is an important step in many environmental studies. A wide variety of methods have been developed in this concern. During this study, remote sensing images were used for environmental impact prediction in Ro-batkarim area, Iran, during the years of 2005～2007. It was assumed that environmental impact could be predicted using time series satellite imageries. Natural vegetation cover was chosen as a main environmental element and a case study. Environmental impacts of the regional development on natural vegetation of the area were investigated considering the changes occurred on the extent of natural vegetation cover and the amount of biomass. Vegetation data, land use and land cover classes (as activity factors) within several years were prepared using satellite images. The amount of biomass was measured by Soil-adjusted Vegetation Index (SAVI) and Normalized Difference Vegetation Index (NDVI) based on satellite images. The resulted biomass estimates were tested by the paired samples t-test method. No significant difference was observed between the average biomass of estimated and control samples at the 5% significance level. Finally, regression models were used for the environmental impacts prediction. All obtained regression models for prediction of impacts on natural vegetation cover show values over 0.9 for both correlation coefficient and R-squared. According to the resulted methodology, the prediction models of projects and plans impacts can also be developed for other environmental elements which may be derived using time series remote sensing images.
