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    Assessment of the suitability of land use for agriculture by analytical hierarchy process: Ahp in lower prachinburi watershed, Eastern Thailand
    (2020-09-01)
    Rukanee, Duangthip
    ;
    Sangchan, Songvoot
    ;
    Choomjaihan, Prasan
    The low Prachinburi watershed is a gateway to other regions of the country. Due to a rapid increase in population there, there is also an increase in agricultural production area. This study aims to assess the appropriateness of land use by using an analytical hierarchy process (AHP) for land use planning. The results of the study revealed that there is a moderate level of the appropriateness in land use development. The most appropriate area (S1) accounts for 22.07%; the moderately appropriate area (S2) accounts for 54.15%; the lowly appropriate area (S3) accounts for 10.25%; and the inappropriate area accounts 13.52% of the area. Regarding a guideline for agricultural area management, it is found to be most appropriate, particularly on field crop growing such as cassava, sugar cane, and maize growing and followed by rice growing and or charding (58.51%, 25.17%, and 6.04%, respectively). Only 10.25% of the total area is inappropriate for farming.
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    Effect of Sustainable Infrastructure Assessments on Construction Project Success Using Structural Equation Modeling
    (2017-05-01)
    Krajangsri, Thanachon
    ;
    Pongpeng, Jakrapong
    Sustainable infrastructure assessments can be used by the construction industry to develop sustainable infrastructure projects and achieve successful construction projects. However, the effect of sustainable infrastructure assessments on the success of construction projects has not been examined in previous studies, based on the authors' knowledge; therefore, this study examined this effect. A questionnaire was used to collect data on the importance of a range of criteria used to evaluate sustainable infrastructure assessments and construction project success. The data were then analyzed using structural equation modeling (SEM) to validate a structural model used to determine the effect of sustainable infrastructure assessments. The final SEM model shows the following: (1) sustainable infrastructure assessments can be described by eight criteria (i.e., environmental impacts on surrounding areas, transport, community, energy and water, location, project management, waste management, and materials and resources); (2) construction project success can be described by six criteria (i.e., environment, quality, safety, time, cost, and client satisfaction); and (3) sustainable infrastructure assessments directly affect construction project success (regression weight of 0.83). The results of this research inform how sustainable infrastructure assessments affect construction project success and could be used as a guideline for developing sustainable infrastructure projects.
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    An Assessment of saline soil effects on land use activities: A case study in Nakhon Panom Province, Thailand
    (2010-02-01)
    Seeboonruang, Uma
    The Northeastern Part of Thailand contains wide area of saline affected soil. Among these, Nakhon Panom Province has received a great deal of attention and thus national budgets in order to raise the economic and social conditions. Many reports have stated that various land uses are likely to be declined due to the salinity problem to some certain extent. However, little to none research has been trying to quantify the effects of such the problem on the land use patterns. This study introduces a simple but practical technique to quantitatively relate the degree of salinity to land use densities. The technique utilizes the multiple linear regression technique to relate the basin salinity index with many land use activities, e.g. community and housing, rice farmings, and livestocks. This method is applied on the subdistrict or "amphoe" units in Nakhon Panom. Initially, the densities of various land use activities are computed based on all secondary data. Subsequently, an equation for basin salinity index (BSI) is formulated in order to figure the severity of salinity problem in specific amphoes. Then, simple linear regression is performed between amphoe BSI and land use densities. Finally, multiple linear regression is obtained linking between the BSI and all the densities. BSI = 23.18 - 176.09 × (population density) - 70.96 × (in-season rice farming density) + 8.17 × (off-season rice farming density) + 55.53 × (cow livestock density) + 3.67 × (poultry livestock density) - 16.19 × (swine livestock density) + 0.00 × (catched fish density). It is found that the salinity has a great negative impact on the population density, in-season rice farming, and swine livestock, while it establishes positive influence on off-season rice farming, cow livestock, and poultry livestock.