Amid concerns that agricultural sectors will be affected by the global climate change, the impact assessments and their countermeasures for developing countries, which are vulnerable to environmental fluctuation, are urgently required. Agricultures in developing countries significantly depend on natural conditions, hence principal production areas are likely to be established in where meet adequate land and climate conditions for cultivation. To assess the impact by climate change to principal production areas, i.e., suitable areas, it is necessary to show the potential land suitability, which is independent from climate condition, spatially and quantitatively. This study aimed to develop the method to assess the land suitability quantitatively for rainy season rice ‘Aman' and dry season rice ‘Boro' in Bangladesh, using a multiple regression analysis and GIS. The model employed six land factors representing Slope, Land Type, Soil Texture, Drainage, Soil Permeability, and Soil Salinity.The distributing areas of 26 attributes representing six land factors in 463 Thana (sub-district) were assumed to the explanatory variables and the productions of Aman and Boro in 2002-2003 were respectively assumed to the response variable, then a multiple regression analysis was executed. The partial regression coefficients were assigned into attribute values for six land factor maps, and potential land productivity per pixel was finally estimated by map calculation in GIS. Adjusted coefficient of determination (adjusted R2) of the multiple regression equations for Aman and Boro were high with 0.903 and 0.823, respectively. Moreover, the total amount of map calculation compiled by new and old provincial boundaries showed a steady accuracy. It implies that the distribution of production for each pixel is reasonable and available as a quantitative index for land suitability.For land suitability assessment, categorical data such as land form and soil type may be appropriate. The method proposed in this study can manipulate them as quantitative variable in such a manner that the areas of each attributes in each administrative boundary are applied for explanatory variables. Since various data completing statistics and map information can be employed by this manner, it provides scalability and versatility for model development.
This paper considers a method to predict global warming impacts on vegetable cultivation. Crop models of major cereals are often used for impact prediction. However, according to previous literature, it is difficult to develop crop models of vegetables because the number of vegetable varieties is much more than that of cereals. Therefore, we consider an alternative method. Results of regression analysis show that air temperature of production environment can explain production costs of some vegetables. This implies that vegetable cultivation at temperatures below or above optimal range has already introduced some adaptation methods, which vary production cost. In order to ascertain substance of adaptation methods, we conduct regression analysis between temperature and itemized production costs. Results show that fertilizer cost, chemical cost, energy and power cost, seedling cost, and management cost are sensitive to temperature and they are thought to be closely related to adaptation methods. Based on the relation between temperature and production cost, global warming impacts on vegetable cultivation will be able to be predicted.