When numerous ducks stepped on rice in the duck paddy field, the blank zone was formed. On the other hand, it was observed that weeds grow thick by a small number of duck introduced. In order to perform appropriate management of the weeds in a duck paddy field, behavior control of a duck group was important. The techniques of measuring the position, the distance between individuals, and moving distance of a duck required for development of a duck simulator were devised. The positions of ducks were photographed as shown in a bird's-eye view with the digital camera. Their positions were measured on the basis of the seedling transplanted elaborately with the rice planting machine. The moving velocity of actual ducks was made into the parameter of the simulation program.The simulation program (Qoid) on the principle of the following three rules was developed. 1) An individual moves at random. 2) An individual moves toward the center of gravity of some neighboring individuals. 3) When their environment gets worse by stay of ducks, an individual searches for good environment. The simulations of duck flock swimming in the paddy field of a small and large scale were performed. Consequently, the virtual duck swam in every nook and corner in the paddy field, and the distance between the individual was close to the actual measurement. Moreover, the size of a sub-group was controllable by adjusting the number of neighboring individuals. The scene in which a virtual duck swims in the paddy field of various form was reproducible. The result of this simulation gave suggestion to the management of an actual duck paddy field.

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We estimated future food demand in urban and rural China at provincial level, considering the impact of demographic factors such as urbanization and aging. The estimation model consisted of two sections: population and food demand. Under population section, we developed a cohort model to estimate the future population. In food demand section, we estimated future food demand per capita by using income as exogenous variable. When data quality appeared questionable, we made adjustments by employing previous studies. The result showed annual demands of grain food and animal food were expected to change from 194 and 60 [million t] in 2005, to 135 and 99 [million t] in 2030, respectively. Urbanization and income growth in rural areas were found to shift the demand from grain to animal protein in the projected period. In the long run, however, the growth rate of animal food demand will decrease as the per capita demand gap become smaller. The increasing number of aged population will also contribute to this reduction.

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To estimate rice yield using remote sensing data, predictive error should be reduced and the variation in estimates caused by fluctuating data should be abated. Furthermore, the extraction of singular data from dataset is beneficial for data analysis. Hence, the bootstrap method is applied to the estimation of comprehensive predictive error given by a regression equation, predictive error for each datum, and the degree of dependence of estimates on fluctuating data. "Unbiased bootstrap predictive error" and "0.632 bootstrap predictive error" are recommended tools for estimating comprehensive predictive error yielded by a regression equation. Moreover, predictive error may be reduced by weighting data using each predictive error. A graphical method of determining each predictive error and the responses of estimates to perturbing data is also shown. The applications of the above techniques to the derivation of regression equations using real remote sensing data and rice yield data give expected results.

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China's continuous rapid economic growth has also spawned a detrimental environmental pollution. The rapid growth caused many changes; expansion of urban areas, urbanization of rural areas, high population density in urban areas. Enhancement of quality of life has been lead the increasing of food waste in municipal solid waste (MSW), but waste treatment infrastructure has not developed in China. Then, there are waste dump in many places, which is a threat to the health of people living nearby and the environment.In China composting is one of the suitable methodｓ for food waste recycling. But, food wastes content oil and salt in wide range depending on the ingredients. There is a possibility that excessive oil and salt could be an inhibitor not only for food waste composting but also for applied plant growth. This study is aimed to demonstrate the effect of oil and salt to food waste composting, and the effect of its compost to the plant Brassica rapa var. peruviridis growth. Food waste samples were taken from university cafeteria, after added oil; from 0 to 36%(dried weight material) and salt; from 0 to 8%(dried weight material) to well mixed food waste samples, put into composting equipment “Kaguyahime” for composting, and analyzed for chemical properties, and used for plant tests. It was found that food waste could contain 36% (dried weight material) of oil for composting. Moreover, the produced compost, containing less than 36% (dried weight material) of oil in food waste material, didn’t inhibit plant growth. Meanwhile, salt could be contained in food waste up to 8% (dry weight material) for composting, and its compost could be applied continuously to soil 1t/10a/y without phytotoxic effects.

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In order to evaluate the dates of bud-burst and leaf defoliation by using the satellite-based Normalized Difference Vegetation Index (NDVI), we need to identify that a certain value of the satellite-based NDVI showed the same phenological stage for every year. In the present study, we estimated the dates of bud-burst and leaf defoliation from 2004 to 2006 in a cool-temperate deciduous broad-leaved forest by using the ground-based NDVI, which was observed by the spectroradiometer situated above the canopy, and the satellite-based NDVI, which was obtained by the MODIS (Moderate Resolution Imaging Spectroradiometer) sensors onboard Terra and Aqua satellites. Then we confirmed thus estimated dates of bud-burst and leaf defoliation with the canopy photographs taken in the forest. The canopy photographs taken on the estimated dates of bud-burst and leaf defoliation showed the similar phenological status of the forest canopy for every year. This means that a certain value of NDVI may indicate the similar phenological stages for every year. The ground- and the satellite-based NDVIs increased in the bud-burst period and decreased in the leaf defoliation period. However, when we estimated the dates of bud-burst and leaf defoliation by using the threshold value, which was the midpoint between the annual maximum NDVI and the annual minimum NDVI, the estimated date of bud-burst was 20 days earlier than the ground observation and the estimated date of leaf defoliation was 32 days later than the ground observation.

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It is presented cartography of 50m grid climate map for monthly air temperature based on air temperature observation of Automated Meteorological Data Acquisition System (AMeDAS) and then it is proposed some utilization of that climate map. Grid data to compile the climate map are estimated by step wise multiple regression analysis using numerous air temperature points data based on AMeDAS observation data, and those values is estimated using the new estimation method proposed by Ueyama. The new method involves estimating the air temperature difference between an AMeDAS observation site and estimation sites; difference of the temperature is partitioned into two contributing factors. It is proposed some utilizations of that 50m grid climate map as useful reference data for assessment of new crop or new cultivation tool in the area, and then as assessment data for growth period in each cultivated field. And furthermore, it is proposed the some utilizations of that map with various existing researches.

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