One of the most important decisions that a forest owner must make is to determine whether a forest stand should be cut or reserved, or when it should be cut in accordance with the estimation of its financial maturity period with the maximization of the total net present value of the whole forestry stand. In this paper, we deal with multistage serial decision making models and we can apply the multistage linear programming method to these problems. This paper deals with a network planning model of silvicultural investment at first, and then discusses how to apply this network planning model, multistage linear programming methods and some applications for deciding the financial maturity period of the individual forest stands. This planning model can provide effective information for deciding the financial maturity period and maximization of the net present value of entire forestry stands under the upper limitations of labor utilization and cutting area and the lower limitation of the forest stands area. According to this planning method, we can predict the long term treatment of forest stands more than 30 years in to the future. This planning model can therefore furnish more effective information on the long term forest management decision making of private forest owners.

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Land suitability is an important factor for regional planning. Geographic Information System is often used for land evaluation, because it enables us to analyze of spatial data. However, for drafting a land use plan, a logical criterion is required to integrate the results of land evaluation. Especially, in an area which is already developed and when urban land use is established, it is necessary to take the former policy and objective of development into consideration for planning. In this study, the regional function in Tochigi Prefecture was analyzed by Principal Component Analysis with some census data representing the productive characteristics in 49 municipalities, and 4 components that were used as commercial, agricultural, industrial and residential general indexes were produced. As a result of categorization by component scores, 22 municipalities were classified into single function category. It implies that the specialization of regional function in Tochigi Pref. had advanced moderately. However, since 8 municipalities were reclassified into agricultural sub-class, it is assumed that they will change to an other category along with urbanization in future. On the other hand, most of the complex areas have a commercial function. Furthermore, using 4 component scores for each municipality, the integration of maps for land evaluation for commercial, agricultural, industrial and residential suitability, which are produced in advance, was examined. By the simple method which overlays the extracted 15% area as the most suitable land from each map, 68.2% in a whole area was unspecified. It is assumed that land evaluation based on natural and traffic factors is likely to be similar and that the same area is selected as suitable land, regardless of different of land use. On the other hand, by integration where the component scores are used as the weight, the superiority of a productive activity is reflected and a suitable area can be subdivided. As a results, unspecified area accounted for 34.7% and it was considered that PCA enables to integrate land evaluation.

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Producer price of sugar beet had been determined by quantity base such as harvested root weight until 1985, while it was changed to quality and quantity base expressed by sugar weight in harvested root since 1986. This change has encouraged sugar beet farmers low input of nitrogen fertilizer. We estimated the amide nitrogen contents of sugar beet at Tokachi Area, Hokkaido, Japan, using Landsat TM data in 1986. The following equation were derived to estimate the amide nitrogen contents successfully: AN = -0.321TM3 + 10.743 …① (n=16, r=0.78^**) where AN is amount of amide nitrogen (meq/100g) and TM3 is Band3 derived Landsat TM data. The excess and deficiency of nitrogen absorption amount was also estimated from amide nitrogen contents and equations (②～⑤), and a sectional map was made. AS₁ = AN x 14.0067 x 10^-5 …② AS₁: amount of amide nitrogen production per 1kg root fresh weight (kg/kg root fresh weight) 14.0067: atomic weight of nitrogen AS₂ = R x AS₁ …③ AS₂: amount of amide nitrogen production per 10ares (kg/10a) R: root weight (kg/10a) EN = 4.1687 + 18.2451 x ln (AS₂+ 1) …④ EN: estimated nitrogen absorption amount (kg/10a) NEL = (19.0 - EN) / 0.78 …⑤ NEL: estimated excess and deficiency of nitrogen absorption amount (kg/10a) 19.0kg/10a: ideal nitrogen absorption amount 0.78: recovery rate of nitrogen fertilizer application These results indicates that estimation of amide nitrogen contents and nitrogen excess or deficiency by Landsat TM data are useful to improve productivity sugar beet by appropriate fertilization, for example.

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In recent years, global warming caused by increasing greenhouse gases has led to international problems. In this study we analyzed documents and articles such as a report on clean energy plan, inter-industry relation table, statistics on energy and economy, comprehensive energy statistics, investigative report on agriculture production cost and so on, and estimated energy consumption and its resultant CO₂ emission in agriculture. The results of this study are summarized as follows: (1) The total Japan's energy consumption in agriculture and forestry in 1991 was 7 x 10⁶ t and the resultant CO₂ emission was estimated at 22 x 10⁶ t. (2) The consumption of petrochemical products and electoricity was 7.894 x 10⁶ KL and 386,870,000 kWh, respectively. Resultant CO₂ emission was found to be 23.3 x 10⁶ t. (3) The CO₂ emission by the production of agricultural materials such as fertilizer, agricultural chemicals and films was estimated to be 4 x 10⁶ t. (4) The study revealed that 7,228 kg of CO₂ per 10a was dischaged from such facilities as greenhouses and hothouses to control their air temperature. (5) CO₂ emission (kg/10a) by agricultural machine operation was 70 for rice, 30 for wheat, 42 for rye, 36 for barley, 75-101 for soy bean, 39-49 for rape, respectively.

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