• Journal of Applied Biological Chemistry
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• v.50 no.2
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• pp.88-96
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• 2007

Organic farming systems based on ecological concepts have the potential to produce sustainable crop yields with no decline in soil and environmental qualities. Recent expansion of sustainable agricultural systems, including organic farming, has brought about need for development of sustainable farming systems based on value judgments for key properties of importance for farming. Chemical and microbiological properties were chosen as indicators of soil quality and measured at soil depth intervals of 5-20 and 20-35 cm in conventional and organic-based apple orchards located in Yeongchun, Gyeongbuk. The orchards were two adjacent fields to ensure the same pedological conditions except management system. Soil pH in organic farming was around 7.5, whereas below 6.0 in conventional farming. Organic farming resulted in significant increases in organic matter and Kjeldahl-N contents compared to those found with conventional management. Microbial populations, biomass C, and enzyme activities (except acid phosphatase) in apple orchard soil of organic farming were higher than those found in conventional farming. Higher microbial quotient ($C_{mic}/C_{org}$ ratio) and lower microbial metabolic quotient for $CO_2(qCO_2)$ in organic farming confirmed that organic farming better conserves soil organic carbon. Biological soil quality indicators showed significant positive correlations with soil organic matter content. These results indicate organic-based farming positively affected soil organic matter content, thus improving soil chemical and biological qualities.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1999.06a
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• pp.53-80
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• 1999

Productivity and environmental conservation in nowadays trade-off and serious problem in agriculture. Precision farming is developing for solving the trade-off problem using systems approach and variable management. The systems approach is attributed to aiming at information-oriented agriculture, environmental-friendly sustainable agriculture, and complex system optimization . The variable management is composed of describing variability , variable-rate technology and decision support system. Three levels of technology development and three farming strategies are introduced for having a prospect. Describing the variability is the first step to promote it. Precision farming could be available for small scale farming as well as big scale farming. Paddy field precision farming will undergo in its distinctive way.

• Journal of Biosystems Engineering
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• v.19 no.3
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• pp.258-273
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• 1994

In this study, an expert system named ESMEF (Expert System for Mechanized Entrusted Farming) was developed. The function of ESMEF is to provide the various data and informations for entrusted farming such as farm machinery management data, mechanization systems by farm sizes, number of units and sizes of machinery needed, machinery replacement analysis, mechanization costs analysis. Mechanization systems were selected by ESMEF for different farming sizes of Chungnam Province and an economic analysis was conducted as an example. The results showed that the farm machinery purchasing costs were 1,344~4,829 thousand won per ha and there was no significant difference for farm sizes above 60 ha. The total annual machinery costs were 3,595~4,537 thousand won per ha, and a minimum cost was appeared for farm size of l00ha at first. According to this analysis, an optimum entrusted farming size would be 100ha by the present available farm machinery systems.

• Journal of Biosystems Engineering
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• v.11 no.1
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• pp.64-75
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• 1986

The objective of this study was to develop a mathematical model for optimum design of diversified farming systems which have the regional characteristics. For this purpose, the farming surveys were conducted for mainly 1984 agriculture. They were carried out on January and July 1985 for three villages of central region of Korea. The surveyed data were analyzed by systems analysis and the diversified farming systems were modeled. They consist of four and six croping patterns for paddy and upland, two and three kinds of fruit crop and livestock, and seven kinds of farm machinery for each work system. Then a mathematical model was developed by the multiple objective decision making (MODM) method in order to design optimum systems of diversified farming. It consists of 23 decision variables, two objective functions and nine constraint functions. The goals of objective function are maximization of agricultural incomes and power inputs of farm machinery, and the modeled factors for constraint function are arable land, available capital, labor, and land utilization.

• Journal of Biosystems Engineering
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• v.11 no.2
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• pp.77-87
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• 1986

This study was carried out to design the optimal systems of diversified farming by the mathematical model developed. In order to achieve this goal, a computer program named DFSDINGP was developed by the nonlinear goal programming(NGP), and for testing its effectiveness, the optimal systems of diversified farming were designed for three regions surveyed and compared them with those of the conventional. DFSDINGP was programmed with FORTRAN 77 and it could handle the NGP problem having 25 independent variables and 75 constraint functions. The study results showed that the developed models and DFSDINGP could design the optimal systems of diversified farming satisfying two goals which are maximum agricultural income and maximum power inputs of agricultural machinery. The agricultural incomes and power inputs of farm machinery of the optimal systems were more than those of the conventional as much as 29-62% and 9-134%, respectively.

• Agricultural and Biosystems Engineering
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• v.5 no.1
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• pp.1-4
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• 2004

Community-based precision farming is a new concept of agricultural systems, which leads to organize groups of wise farmers and technology platforms in Japan. The wisdom farmers create a rational farming system to manage hierarchical variability: variability in farmers' community as well as variability of within-field and between-field. The technology platform develops and provides three key-technologies: mapping technology, variable-rate technology, and decision support systems available for rural constraints. Advancement of bio-production robots leads precision farming to the next level, where two technological innovations: how to produce and manage information-oriented fields and information-added products, can be attained.

• Korean Journal of Organic Agriculture
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• v.20 no.2
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• pp.143-159
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• 2012

Agricultural income is calculated with producer price, output and management cost. This study compared organic farming with conventional one for agricultural income, producer price and output by items. And then it proposed the method of item selection and crop system from a diversification point of view. The coefficient of variation to producer prices in organic farming was 4.7%, and conventional one was 30.3% because organic products have been produced in a system of contract farming with consumers' cooperative. This result means the price of organic products is stabler than that of conventional price. And agricultural income of organic farming has been generally known more than that of conventional one. However, agricultural gross income of conventional farming was more than that of organic one by 20.3% in 2010. It was caused by output reduction of a few items(fer example; onion, large green onion, potato and young pumpkin) due to freak weather conditions and constant producer price for several years in organic farming. In order to increase agricultural income, appropriate crop selection and system should be introduced to organic farming. A principal crop is the rice plant and 2 subordinate crops are dry crops at bare field and greenhouse respectively. Thus 5 crop systems that agricultural gross income are relatively increased larger among 15 crop systems estimated are rice+ginger+cucumber, rice+ginger+tomato, rice+large green onion+cucumber, rice+sweet potato+cucumber and rice+onion+ cucumber.

• Korean Journal of Organic Agriculture
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• v.12 no.2
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• pp.135-151
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• 2004

U.S. organic farming has grown rapidly-20 percent or more annually-throughout the 1990s, which kept pace with consumer demand for organically produced food. Thus certified organic acreage is the total to 235 million acres in 48 state in 2001, and SO the U.S. ranked fourth in land area managed under organic farming systems. And according to several surveys, consumer's reasons for purchasing organic food are health and nutrition, taste and environmental concerns. California and North Dakota were the top two states in 2001 for certified organic cropland; the former with mostly fruits and vegetables, and the latter with wheat, soybeans, and other crops. And the top two states for certified organic pasture were Colorado and Texas. And then several states such as Iowa and Minnesota have begun subsidizing conversion to organic farming systems as a way to capture the environmental benefits of these systems. The price of organic produce fluctuates rather broadly because of being traded by market economy principle and of demand-supply disequilibrium. Nevertheless, average price premiums for organic produce are higher than the prices for the produce under conventional farming. Future prospects for U.S. organic farming are as follows; Demand for organically grown foods is expected to continue growing at a rapid pace, as more growers convert to organic production and more processors and distributors expand organic selections in their product lines. And new processed products and new types of healthy foods are likely to appear on the market, and some new organic products will be aimed at mainstream markets.

• Korean Journal of Organic Agriculture
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• v.19 no.spc
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• pp.74-77
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• 2011

The production practices, productivity and economic performance of organic and non-chemical rice farming were compared in Jeonnam Province, Korea. Korean organic rice farming showed a lack of use of resistant varieties and rotational cropping systems as well as less use of farm wastes and a high dependency upon external inputs. When compared with no-chemical rice production practices very little differences were found. However, organic rice farming showed 15% to 18% higher profits than no-chemical farming even though the productivity was arguably similar between the two farming types. This may encourage more farmers to convert to organic production rather than non-chemical farming as the farming practices are very similar, thereby resulting in increased supply of organic products and decreased prices for organic rice near future. There is a need to more greatly differentiate organic farming practices and products from those of no-chemical farming.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.4
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• pp.180-186
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• 2022

Agrophotovoltaic (APV) system is an integrated system producing crops as well as solar energy. Because crop production underneath Photovoltaic (PV) modules requires delicate management of crops, smart farming equipment such as real-time remote monitoring sensors (e.g., soil moisture sensors) and micro-climate monitoring sensors (e.g., thermometers and irradiance sensors) is installed in the APV system. This study aims at introducing a decision support system (DSS) for smart farming in an APV system. The proposed DSS is devised to provide a mobile application service, satellite image processing, real-time data monitoring, and performance estimation. Particularly, the real-time monitoring data is used as an input of the DSS system for performance estimation of an APV system in terms of production yields of crops and monetary benefit so that a data-driven function is implemented in the proposed system. The proposed DSS is validated with field data collected from an actual APV system at the Jeollanamdo Agricultural Research and Extension Services in South Korea. As a result, farmers and engineers enable to efficiently produce solar energy without causing harmful impact on regular crop production underneath PV modules. In addition, the proposed system will contribute to enhancement of the smart farming technology in the field of agriculture.