抄録
Objective. Population growth in the world seems to raise the demand of water and food. However water resource is limited, which may arise water deficiency in several areas or countries. Particularly, agricultural water scarcity will accelerate the expansion of undernourishment damage caused by insufficient food supply. The aim of this study is to model undernourishment damage caused by agricultural water scarcity based on statistical analysis.
Results and Discussion. At first, food productivity due to agricultural water shortage was estimated based on the relationship between annual harvest of each crop and agricultural water use on a country scale. Subsequently, non-linear multiple regression analysis among undernourishment damage data and three explanatory variables (the average daily dietary energy consumption per capita, the Gini coefficient of dietary energy consumption and the health expenditure per capita) was performed based on statistical data of each country. As a result of the analysis, every selected explanatory variable was statistically significant, and undernourishment damage caused by the shortage of unit volume agricultural water (damage factors) was estimated for each country. The results of estimated damage factors showed that countries with low agricultural water supply capacity would have a high sensitivity to agricultural water deficiency. Furthermore, preliminary prediction of damage factors in 2050 indicated that population growth would certainly shift damage factors larger particularly in the several African countries. According to the comparison of the health damage factors related to domestic use water scarcity in the previous study, many countries in the African region seem to be sensitive to both agricultural and domestic water scarcity.
Conclusions. Undernourishment damage caused by unit agricultural water scarcity can be estimated quantitatively. On the other hand, the analysis was conducted based on country scale data, so the diversity of local situation in each country was not considered in the study. In order to improve the precision of damage factors, more detailed analysis based on data of local scale should be conducted and dynamic changes of important factors like food productivity and water resource amounts should be taken into account.
