Based on the survey data of 250 farmers from the Multan district of Southern region of Punjab, Pakistan this study aims to empirically examine the determinants of access to agricultural credit. This study used the probit model to analyze the data. The results revealed that formal education, farm size, level of farm mechanization, farm revenue and landholding size positively and significantly influenced access to agricultural credit while the age of the farmer’s, distance, and off- farm income negatively and insignificantly influenced farmer’s accessibility to agricultural credit. The findings of the current study offer a policy guideline to streamline national policy on agricultural finance. This study also recommends that ZaraiTaraqiati Bank (ZTBL) and other Commercial Banks should improve their agricultural credit schemes to fulfil the diversified needs of small farm holders.
Geotechnical and Geological Engineering - Due to the complexity of the interaction between the geogrid and the soil interface in high earth-rock dams, the method of replacing the grid with steel... 相似文献
Natural Hazards - A 2D local inertial equations model coupled with a 1D hydraulic model was established to simulate flood dispatching in river and flood detention areas. A simplified first-order... 相似文献
Natural Hazards - Urban flood inundation is worsening as the number of short-duration rainstorms increases, and it is difficult to accurately predict urban flood inundation over a long lead time;... 相似文献
Identifying and analyzing the urban–rural differences of social vulnerability to natural hazards is imperative to ensure that urbanization develops in a way that lessens the impacts of disasters and generate building resilient livelihoods in China. Using data from the 2000 and 2010 population censuses, this study conducted an assessment of the social vulnerability index (SVI) by applying the projection pursuit cluster model. The temporal and spatial changes of social vulnerability in urban and rural areas were then examined during China’s rapid urbanization period. An index of urban–rural differences in social vulnerability (SVID) was derived, and the global and local Moran’s I of the SVID were calculated to assess the spatial variation and association between the urban and rural SVI. In order to fully determine the impacts of urbanization in relation to social vulnerability, a spatial autoregressive model and Bivariate Moran’s I between urbanization and SVI were both calculated. The urban and rural SVI both displayed a steadily decreasing trend from 2000 to 2010, although the urban SVI was always larger than the rural SVI in the same year. In 17.5% of the prefectures, the rural SVI was larger than the urban SVI in 2000, but was smaller than the urban SVI in 2010. About 12.6% of the urban areas in the prefectures became less vulnerable than rural areas over the study period, while in more than 51.73% of the prefectures the urban–rural SVI gap decreased over the same period. The SVID values in all prefectures had a significantly positive spatial autocorrelation and spatial clusters were apparent. Over time, social vulnerability to natural hazards at the prefecture-level displayed a gathering–scattering pattern across China. Though a regional variation of social vulnerability developed during China’s rapid urbanization, the overall trend was for a steady reduction in social vulnerability in both urban and rural areas.