Recently, the increased rate of developments in suburban areas has resulted in marked decrease of farmland. Farmland has various functions and plays an important role in our life. Therefore, it is necessary to promote conservation of farmland. In this regard, in order to consider and adopt an appropriate policy to prevent the decrease in farmland, it is important to make a detailed analysis of land use and predict the future land use based on this analysis. In this study, we analyzed the land use pattern of a study area in Osaka, Japan, considering social factors comprehensively. Further, we constructed a Naive Bayes probabilistic model to predict details of the future land use, for consideration in future land use policy formulation. The results of our analysis suggested that neighborhood land use and low restrictions on developments in the area are significant and important factors influencing changes in farmland use in the study area. The accuracy of the model was validated by comparing the predicted land use with the real land use of the area. The recall ratio of the model was more than 70%, which is higher than the previous models. Consequently, it is predicted that if the present condition continues, there will be considerable decrease in farmland area in the future compared to the target value originally set by the municipality in Osaka. It is suggested that the preferential restriction on development of dense farmland by tightening law more than ever is an effective measure in lowering the rate of decreasing farmland in the area to some extent.
Agricultural statistics is fundamental reference for evaluating damage of a natural disaster, estimating food supply and demand, and framing a policy. The remote sensing technique is necessary for estimating the area of agricultural land in a large region or a difficult area for an on-site survey. The authors developed a method to classify land-use/land-cover, especially paddy field, using the modified normalized difference water index (MNDWI) and the normalized difference vegetation index (NDVI) derived from satellite optical sensor data. This method was applied to the Landsat TM (Thematic Mapper)/ETM+ (Enhanced Thematic Mapper Plus) data to detect paddy fields in Aomori Prefecture. The area of paddy field in each district in 2002 was estimated from the area ratio of paddy field in each mixel consisting of paddy field and other categories. The area ratio 100% of paddy field in a mixel was determined to be an average MNDWI - 3σ of water areas in the rice-planting season (0.15 for the Path 107 and 0.10 for the Path 108); the area ratio 0% of paddy field, an average MNDWI + 2σ of ground and built-up (-0.17). As the result, the total area of paddy field in Aomori Prefecture in 2002 was estimated to be 51,283 ha, which was 97.5% of the statistics (52,597 ha). The accuracy of paddy field classification was 93.0-97.7% and that of paddy field detection was 85.0-97.0%. It was shown that the simple classification method developed in this study could reduce working time for the land-use/land-cover classification, comparing with unsupervised or supervised classification such as the clustering or the most likelihood method.
The grasslands of Aso region have long been maintained by controlled burning and cattle grazing. In the present study, the effects of controlled burning and grazing on the grassland maintenance was quantitatively investigated using satellite images. Data on practices of controlled burning and cattle grazing for 170 pasture lands around Mt. Aso were collected. Landsat images of the pasture lands in three periods (Period I: before burning, Period II: after burning, and Period III: after grazing) from 1999 to 2014 were used for the analysis, and in each image, mean NDVI (Normalized Difference Vegetation Index) was calculated for each pasture land. The effects of year, burning, grazing and burning-grazing interaction on the NDVI of each period and the NDVI differences between two periods were investigated. In all analyses, the effects of year and burning on NDVI were significant (p<0.01). The decreases of NDVI in the burned pasture lands were observed, indicating that burning may have the potential to impede the grassland succession to forest. As for grazing, there were significant effects of grazing on the NDVI in Period II and the NDVI difference between Periods II and III (p<0.05). In Period II, NDVI were greater in the pasture lands where grazing was conducted in the previous year, suggesting that feces and urine from grazing cattle might have promoted growth of vegetation. The significant NDVI decreases from Periods II to III in the grazed pasture lands might have been resulted from the decreases in the amount of plant biomass by cattle grazing. The interaction effect of burning and grazing was also significant in Period III (p<0.05), and the effect of burning was only significant in the grazed pasture lands. These results indicated that burning and grazing in combination with burning be useful for grassland maintenance in the region.
The Japanese Agricultural Systems Society was established in April, 1984 and the journal of the Society has published across a wide range of disciplines and at various levels of detail concerning agricultural systems since September, 1984. The Society and the journal have emphasized the need for interdisciplinarity in agriculture and aimed to integrate research methods of the social and natural sciences. In this paper, four different research styles were identified in social and natural sciences based on the difference of available data (qualitative vs. quantitative data) and research methods (explanatory vs, descriptive approaches) and it was suggested that agricultural system researches should accept all of the four research styles. Furthermore, framework and scope of agricultural system researches was discussed and a new direction of agricultural system research was demonstrated using system biology in life science as an example.