This article offered a concise survey concerning analysis tools in the field of agricultural system researches. The first part outlined conventional analysis tools: systems simulation, mathematical programming and systems engineering. The second part showed new analysis tools such as life cycle assessment (LCA), material flow analysis and use of remote sensing, global positioning system (GPS) and geographic information system (GIS).
This paper summarizes system dynamics, tools for construction and execution of system dynamics, and applications to agricultural systems. System dynamics is an approach to simulation originally developed by Forester at MIT. Forester initially focused his approach on industrial systems, but now this approach is applied to many fields, containing ecosystems and agricultural systems. The system description is translated into the level and rate equations of a system dynamics model. “Stella” and “Vensim”, which are software for construction and execution of system dynamics, have good graphical user interface. The purpose of modeling is understanding and prediction. In agriculture, prediction of yield or plague of harmful insects is important. If predicted results by models are not suitable, managements are changed so as to take better results.
Evaluation of whole animal production systems from various environmental viewpoints has been required to reduce environmental impacts of animal production systems, since an increasing environmental consciousness in society requires action by the animal industry on environmental problems. The life cycle assessment (LCA) method, which evaluates the environmental impacts associated with a product, process or activity during its life cycle by describing its requirements for resource and the emissions, is expected to be highly effective for such evaluations. And studies about evaluation of using LCA and development of evaluation methods based on the LCA concept for animal production systems have been conducted in recent years. In this paper, we outlined the methodology of LCA, and reviewed LCA studies on animal production reported. Furthermore, we presented the studies that evaluated beef production and feeds prepared from food residues as examples of our research about environmental impact evaluation of animal production systems, and finally described challenges and future works in the research area of animal production LCA.
There are many methodologies to analyze or evaluate agricultural production systems. The methods or tools to select are naturally different according to aim of evaluation and to characteristic of objective systems. Some introduced researches in this report were a method with entropy and a multiple evaluation with five indexes. The farmer in which the concept of resource physics was applied focused on the waste side of the agricultural production systems. Layer production was selected as an objective system and simulation of economics, energy and entropy was performed for different regions and housing types to demonstrate. To the same objective systems, it was tried to express sustainability of system by using entropy production for linear system. The latter with 5 indexes which were economic balance, fossil energy input, nitrogen load, animal welfare and satisfaction rating of farmer, was applied to dairy production systems. Statues of H-district and S-district were indicated by plotting values of five indexes on a radar chart.
Today, it becomes a serious national problem that the amount of wild beast damages for crops is increasing. The effects are not only monetary lose but also decreasing of farming. The reason why the wild beast damages become conspicuous is debilitation of farm labor construction. The pullout from farming, aging of farmer and following as a side job put some distance make between farmers and farm. Moreover, the cultivation abandon fields along the mountainside make fuzzy the border between human and wild beast. The geographic information systems(GISs) are effective for collecting information and aggregating them. This paper introduces the situation and policy of wild beast damages in Kyoto, and discusses the possibility of applying GISs. After that, the GISs apply for confirmation of “decreasing effect” of wild beast damages by grazing project. This “decreasing effect” means decreasing wild beast damages by grazing at bamboo area neighboring farm. Finally, the problems for using GISs more effectively in wild beast policy were discussed. Those are (1) the construction of wild beast damage map which aggregated various data maps and (2) the update and community of the wild beast damage map.
Forest is a place of forestry activity. In addition, it has recently drawn public attention with its important function such as absorbing carbon dioxide, regulating its atmospheric concentration and subsequently affecting global warming, and maintaining high biodiversity. However, Japanese forestry is currently on the decline and, due to an increase in poorly controlled forests, the deterioration of these function is feared. Therefore, a way to efficiently manage forests with lower cost should be suggested for mitigating this situation. High-resolution remote sensing tools, which can collect detailed data for large areas, can be used effectively for this purpose. So, methods to extract forest data, such as forest density and tree heights, have been well examined. However, there has not been many researches carried out using these data and standard research methods using these data has not been established yet. Hence, it is significant to develop a new and effective method using these data for future research. In this study, we examined algorithm to classify forest types, and we classified forest into four types; natural conifer, natural broadleaved trees, artificial conifer and artificial broadleaved trees. The classification was carried out using spectral features obtained from IKONOS data, tree heights from LiDAR data, and texture features. The texture features used here were five variables that were the average, the contrast, dispersion, energy and entropy of R, G, B, Nir, NDVI. When the features were calculated, three rules were applied to each value of IKONOS and LiDAR data. The three rules were the followings. First, the section was between the maximum and the minimum in the histogram of the original data. Second, groups of cells (we call “window”) had four sizes; 3x3, 5x5, and 7x7. Final rule was that there were 2 types in window shape, namely rectangle and diamond shapes. The use of diamond shapes was a brand new method suggested in this study, and it was shown to improve the accuracy in the classification of natural conifer with this method. As a result of the analysis, the optimal size and shape of window, which gave the highest accuracy in the classification with texture features, were decided. The optimal shape was diamond-shape and the size was 7x7 divided into 7. Although the average classification accuracy of the whole area analyzed was only 61.3%, the accuracy of the natural conifer area was as high as 86.6%. This data used as much as 30 variables in Multi-Variate Analysis. However, even after decreasing the number of variables by Principal Component Analysis, the accuracy in the classification did not change largely. It was shown to be possible to decrease the number of variables in Multi-Variate Analysis without lowering the classification accuracy.
A water-saving cultivation technology for tomatoes has been developed in Nong Saeng village, Khon Kaen Province, Northeast Thailand. The technology utilizes the water stored in the soil during the rainy season. By using this technology, it is possible to reduce the amount of irrigation water used to less than 5 mm per crop cycle. In this area, the standard irrigation level is more than 500 mm. Since this technology is considered to depend on soil moisture, we demonstrated the mechanisms involved by using a soil moisture simulation. Further, we evaluated the applicability of the technology to other soil types in Northeast Thailand. In brief, the method we used was as follows. In the first step, we simulated the actual soil moisture dynamics from the end of the rainy season to the period of transplanting. In the next step, we carried out identical simulations for four soil types typical to Northeast Thailand. On the basis of the results obtained, we evaluated the applicability of the water-saving technology by comparing the water supply capacity of each soil type. For the analysis, we used the HYDRUS-1D simulation code, which has often been used in the agricultural field. The initial soil condition at the end of the rainy season (September 23, 2004) was designated as the saturated condition. The parameter for the dry sand layer effect was adjusted so that can be equal to the soil moisture condition of the transplanting time (December 1, 2004). The actual evaporation observed during this period was 150 mm. It was enough big dry sand layer effect to the 420 mm of the potential evaporation. Further, using these parameters, we simulated the soil conditions present from the time of transplanting to the time of harvesting. The results obtained were similar to the soil moisture dynamics that we had measured. Lastly, we simulated the soil moisture dynamics in the four soil types that are typical to Northeast Thailand （Nam Phong, Ubon, Roi-Et, Phimai）. We compared the water supply capacities of these soil types. We observed that the technology was more suitable for Roi-Et and Phimai than for Nam Phong, where it was developed. The suitability of Ubon for the usage of this technology was lower than that of Nam Phong; however, the difference between the two was small. These results demonstrate that this water-saving technology can be applied to a large area of Northeast Thailand.
Use of weighted regression is attempted for deriving regression equations which aim at estimating rice yield by satellite data give by ASTER sensor. It is assumed that satellite data and rice yield data are obtained in 2001 and 2002, and in 2004, all satellite data and a part of rice yield data are given. Then, the unobserved rice yield data in 2004 is to be estimated. For this purpose, multiple regression equations are obtained and predictive errors are estimated. The results indicates that use of weighted regression allows us to make the most of three-year data for beneficial estimation. The procedure suggested here is an attempt to utilize weighted regression for realizing regression which reflects the degree of validity of respective data, as well as a method for coping with inhomogeneity of variance of data. Moreover, use of additive model instead of multiple regression equation confirms the effect of weighted regression. The results given by additive model are shown to be more desirable than those by multiple regression equation.