• Korean Journal of Agricultural Science
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• v.26 no.2
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• pp.77-93
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• 1999

This study is aiming at identifying the foreward and backward lingkage effects of the farm land base development project. Korean Government has continuously carried out farmland base development projets including the integrated agricultural development projects. large and medium scale irrigation projects and the comprehensive development of the four big river basin including tidal land reclamation and estuary dam construction for the all weather farming since 1962. the starting year of the five year economic development plans. Consequently the irrigation rate of paddy fields in Korea reached to 75% in 1998 and to escalate the irrigation rate, the Government had procured heavy investment fund from IBRD. IMF and OECF etc. To cope with the agricultural problems like trade liberalization in accordance with WTO policy, the government has tried to solve such problems as new farmland base development policy, preservation of the farmland and expansion of farmland to meet self-sufficiency of foods in the future. Especially, farmland base development projects have been challanged to environmental and ecological problems in evaluating economic benefits and costs where the value of non-market goods have not been included in those. Up to data, in evaluating benefits and costs of the projects, farmland base development projects have been confined to direct incremental value of farm products and it's related costs. Therefore the projects'efficiency as a decision making criteria has shown the low level of economic efficiencies. In estimating economic efficiencies including Leontiefs input-output analysis of the projects could not be founded in Korea at present. Accordingly this study is aimed at achieving and identifying the following objectives. (1) To identify the problems related to the financial supports of the Government in implementing the proposed projects. (2) To estimated backward and foreward linkage effects of the proposed project from the view point of national economy as a whole. To achieve the objectives, Hwangrak benefited area with reservoir which is located in Seosan-haemi Disticts, Chungnam Province were selected as a case study. The main results of the study are summarized as follows : a. The present value of investment and O & M cost were amounted to 3,510million won and the present value of the value added in related industries was estimated at 5.913million won for the period of economic life of 70 years. b. The total discounted value of farm products in the concerned industries derived by the project was estimated at 10,495million won and the foreward and backward linkage effects of the project were amounted to 6,760 and 5,126million won respectively. c. The total number of employment opportunities derived from the related industries for the period of project life were 3,136 man/year. d. Farmland base development projects were showed that the backward linkage effects estimated by index of the sensitivity dispersion were larger than the forward linkage effect estimated by index of the power of dispersion. On the other hand, the forward linkage effect of rice production value during project life was larger than the backward linkage effect e. The rate of creation of new job opportunity by means of implementing civil engineering works were shown high in itself rather than any other fields. and the linkage effects of production of the project investment were mainly derived from the metal and non-metal fields. f. According to the industrial linkage effect analysis, farmland base development projects were identified economically feasible from the view point of national economy as a whole even though the economic efficiencies of the project was outstandingly decreased owing to delaying construction period and increasing project costs.

• Korean Journal of Remote Sensing
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• v.33 no.5_2
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• pp.699-708
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• 2017

The present study was to develop an approach for predicting soybean yield using a crop growth simulation model at the regional level where the detailed and site-specific information on cultivation management practices is not easily accessible for model input. CROPGRO-Soybean model included in Decision Support System for Agrotechnology Transfer (DSSAT) was employed for this study, and Illinois which is a major soybean production region of USA was selected as a study region. As a first step to predict soybean yield of Illinois using CROPGRO-Soybean model, genetic coefficients representative for each soybean maturity group (MG I~VI) were estimated through sowing date experiments using domestic and foreign cultivars with diverse maturity in Seoul National University Farm ($37.27^{\circ}N$, $126.99^{\circ}E$) for two years. The model using the representative genetic coefficients simulated the developmental stages of cultivars within each maturity group fairly well. Soybean yields for the grids of $10km{\times}10km$ in Illinois state were simulated from 2,000 to 2,011 with weather data under 18 simulation conditions including the combinations of three maturity groups, three seeding dates and two irrigation regimes. Planting dates and maturity groups were assigned differently to the three sub-regions divided longitudinally. The yearly state yields that were estimated by averaging all the grid yields simulated under non-irrigated and fully-Irrigated conditions showed a big difference from the statistical yields and did not explain the annual trend of yield increase due to the improved cultivation technologies. Using the grain yield data of 9 agricultural districts in Illinois observed and estimated from the simulated grid yield under 18 simulation conditions, a multiple regression model was constructed to estimate soybean yield at agricultural district level. In this model a year variable was also added to reflect the yearly yield trend. This model explained the yearly and district yield variation fairly well with a determination coefficients of $R^2=0.61$ (n = 108). Yearly state yields which were calculated by weighting the model-estimated yearly average agricultural district yield by the cultivation area of each agricultural district showed very close correspondence ($R^2=0.80$) to the yearly statistical state yields. Furthermore, the model predicted state yield fairly well in 2012 in which data were not used for the model construction and severe yield reduction was recorded due to drought.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1341-1352
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• 2023

The normalized difference vegetation index (NDVI) derived from satellite images is a crucial tool to monitor forests and agriculture for broad areas because the periodic acquisition of the data is ensured. However, optical sensor-based vegetation indices(VI) are not accessible in some areas covered by clouds. This paper presented a synthetic aperture radar (SAR) based approach to retrieval of the optical sensor-based NDVI using machine learning. SAR system can observe the land surface day and night in all weather conditions. Radar vegetation indices (RVI) from the Sentinel-1 vertical-vertical (VV) and vertical-horizontal (VH) polarizations, surface elevation, and air temperature are used as the input features for an automated machine learning (AutoML) model to conduct the gap-filling of the Sentinel-2 NDVI. The mean bias error (MAE) was 7.214E-05, and the correlation coefficient (CC) was 0.878, demonstrating the feasibility of the proposed method. This approach can be applied to gap-free nationwide NDVI construction using Sentinel-1 and Sentinel-2 images for environmental monitoring and resource management.