Pesticides are widely used for the cultivation of agricultural products in Japan. However, there is a risk that sprayed pesticides will drift toward nontarget crops, a phenomenon known as drift hazard. In the present study, a simplified method for both measuring and evaluating spray drift was developed using an image-processing system. Experimental pesticide spraying was conducted using a boom sprayer, a large fan, water-sensitive paper, a diluted solution of Sumithion (MEP) emulsion, stimulant crops, and glass plates. The diluted MEP emulsion was sprayed following the placement of the water-sensitive paper, stimulant crops, and glass plates at arbitrary measurement points, from which each material was then collected. Images of the collected water-sensitive paper were scanned and saved as red-green-blue (RGB) image data. Subsequently, the cover-area ratio was calculated from the RGB image data by using the following methods: decomposition of RGB components, extraction processing, binary conversion based on the brightness threshold, and arithmetic processing of pesticide stains. When only R-image data were used, the cover-area ratio was calculated with a high degree of accuracy, while the measurement time of the image-processing algorithm was greatly reduced. This image-processing algorithm provides a simple, objective, and repeatable means of measuring and evaluating spray drift by using water-sensitive paper.

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The purpose of the present study was to find potential areas for forest production using Spatial Data Mining (SDM) technique, GIS, and remote sensing. Artificial Neural Network (ANN) was the data mining method used. Current land use, average annual rainfall, soil erosion, slope, and fire hazards were selected as the criteria for the forest production. For this study, an ASTER-VNIR (Level 1B) satellite image acquired on 11 July 2006 was used. The image was classified into eight land use classes. Digital Elevation Model (DEM) was used to derive the slope map (20 m). The normalized Difference Vegetation Index (NDVI) was applied to detect areas of vegetation cover. A model was developed to evaluate areas vulnerable to soil erosion. Thematic paper maps (rainfall and soil) of the study area were screen digitized and converted to the raster format. The Alyuda NeuroIntelligence 2.2 application was used to implement the standard Back-Propagation (BP) algorithm for ANN modeling. Training stage of ANN helped to identify the optimal neural network structure. The final result was saved in spreadsheet format and converted to the GIS format for the output process, which was evaluated to produce the final map. This map was used to separate the suitable areas for the forest production into three suitability classes. The results showed that 19.65%, 28.43%, and 51.93% land areas were most suitable, suitable and less suitable, respectively, for the forest production. The SDM is more powerful than conventional cartography modeling technique for land suitability analysis.

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This study examined the potential predictability of paddy rice yield variation on a local scale (approximately 2 km × 2 km) by using a prefectural-scale dynamic paddy rice simulation model (the PRYSBI model) with the local observed weather data, taking the 160 local areas in Tochigi Prefecture, Japan, as the study area. From the comparison of the simulated and observed local yields during the 11-year period (1993, 1995, 1998-2006), the PRYSBI model showed high capability to simulate the interannual variation of area-mean local yield over the study area with the quite large root-mean-square error (RMSE) of 2.1 Mg ha^-1. However, the RMSE has the statistical-significant relationship with the local areal features on agriculture. We thereby incorporated the local areal features in the simulated yields in the manner of the multiplicative model approach. The parameter values of the multiplicative model were estimated by using the Bayesian approach, which can count the uncertainty of parameter value in a stochastic manner. By this means, the potential predictability in terms of the coefficient of determination (r²) and RMSE between the simulated and observed local yields improved from r²=0.430 to r²=0.527 and from RMSE=2.0 Mg ha^-1 to RMSE=0.4 Mg ha^-1 compared to the PRYSBI model alone. The potential predictability of local yield could improve by incorporating the local areal features in the output of crop model.

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The release of pet raccoons (Procyon lotor), a North American native, has resulted in the species becoming naturalized in most of Japan's prefectures. When state and local governments implement wildlife management programs including an alien species, it is important to know the size of the target population to be managed. The population estimation for an alien species over an extensive area, such as raccoon, has not yet been developed, In this study, we developed a raccoon population model using two statistical methods: a generalized linear model (model 1) and a generalized linear mixed-model (model 2) were analyzed using geographic information system (GIS) data based on harmful animal extermination data, land-use data, and satellite data. The explanatory variable of the natural environment was elucidated by a stepwise approach in both models. Based on the two models, the population distribution of raccoons within Osaka Prefecture was estimated using GIS. Model estimates were highly correlated with actual data on the presence of raccoons (model 1: r = 0.898, model 2: r = 0.898). Model 1 estimated the size of the raccoon population in Osaka Prefecture to be 2,836 individuals (95% CI = 2,350-3,418) and model 2 estimated it to be 2,836 individuals (95% CI = 2,289-3,515).

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The purpose of this study is to develop a financial accounting software according to the characteristics of agricultural enterprise accounting, which can strengthen the management of the agricultural enterprise and relieve the accountant from tedious accounting information processing. The system was developed by using Microsoft Visual Basic 6.0 as user interface and Microsoft Access 2003 to save the accounting data, with the operating system of Microsoft Windows XP. Company X tested the system with the survey data. It was observed that the system had excellent user friendly interface, as well as had the following characteristics. Firstly, in the system the user could operate the accounting subjects, manage accounting evidence and accounting entry freely and easily. Secondly, the system could auto generate financial reports and some subsidiary schedules. Thirdly, the system had powerful capabilities of storing the accounting data, as a result the system managed the historical information promptly and efficiently. Finally, the system was capable of relieving the accountant from tedious accounting calculation, saved time in accounting data input and reduced the mistake caused by input and calculation.

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