• KOREAN JOURNAL OF CROP SCIENCE
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• v.53 no.1
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• pp.1-7
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• 2008

This experiment was carried out to evaluate the agronomic stability of North Korean rice varieties using the statistical model developed by Grafius, Finlay, and Ever hart. The lowest yearly variation based on coefficients of variation was found in Hannam 29 for number of panicles per hill, in Sijoong 9 for number of grains per panicle, in Pyeongyang 3 for ripened grain ratio, in Sijoong 16 for 1,000 grain weight, and in Yeomju 1 for grain yield. By Grafius's model, Pyeongbook 3, Weonsan 66 in early maturing groups and Seohaechalbyeo in medium maturing groups show stable for 3 years. Weonsan 66 in early maturing groups and Seohaechalbyeo in medium maturing groups were found to be highly stable as analyzed by both Finlay and Wilkinson's model and Everhart & Russell's model. With reference to three models, Weonsan 66 was highly stable for 3 years with showing more yield than Odaebyeo in early maturing groups while Seohaechalbyeo was highly stable for 3 years with showing high yield than Hwaseongbyeo in medium maturing groups above $5\;t\;ha^{-1}$ of milled rice respectively.

• Asian-Australasian Journal of Animal Sciences
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• v.22 no.1
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• pp.57-71
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• 2009

Crop-livestock mixed farming systems depend on the efficiency with which nutrients are conserved and recycled. Home-grown forage is used as animal feed and animal excretions are applied to cultivated crop lands as manure. The objective of this study was to develop a mixed farming system model for dairy cattle in Japan. The model consisted of four sub-models: the nutrient requirement model, based on the Japanese Feeding Standards to determine requirements for energy, crude protein, dry matter intake, calcium, phosphorus and vitamin A; the optimum diet formulation model for determining the optimum diets that satisfy nutrient requirements at lowest cost, using linear programming; the herd dynamic model to calculate the numbers of cows in each reproductive cycle; and the whole farm optimization model to evaluate whole farm management from economic and environmental viewpoints and to optimize strategies for the target farm or system. To examine the model' validity, its predictions were compared against best practices for dairy farm management. Sensitivity analyses indicated that higher yielding cows lead to better economic results but higher emvironmental load in dairy cattle systems integrated with forage crop production.

• Magazine of the Korean Society of Agricultural Engineers
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• v.39 no.6
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• pp.31-40
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• 1997

Potential impacts for unfavourable weather conditions and the assessment of the magnitudes of their adverse effects on crop yields were studied. EPIC model was investigated for its capability on crop yield predictions for rice and soybean. Weather generationmodel was used to generate long-term climatic data. The model was verified with ohserved climate data of Suwon city. Fifty years weather data including abnormal conditions were generated and used for crop yield simulation by EPIC model. Crop yield probability function was derived from simulated crop yield data, which followed normal distribution. Probable crop yield reductions due to abnormal weather conditions were also analyzed.

• Proceedings of the Korean Society of Crop Science Conference
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• 2004.04a
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• pp.104-105
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• 2004

[ $\bigcirc$ ] In the phenology model of ORYZA2000, the effect of photoperiod on the developmental rate was a little ignored because most crop parameters were measured with IRRI varieties which are insensitive to photoperiod, therefore it is very difficult to apply this phenology model directly to Korean varieties which are usually sensitive to photoperiod. $\bigcirc$ After introducing PPFAC and PPSE to improve the phenology model, the precision of heading date prediction was improved but not satisfied. $\bigcirc$ In the growth simulation using data from several regions, yield tended to be overestimated under high nitrogen applicated condition. $\bigcirc$ The precision of yield was much improved by introducing nitrogen use efficiency, but still different between regions because of different soil fertility or property of irrigation water between regions

• Korean Journal of Remote Sensing
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• v.35 no.1
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• pp.57-81
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• 2019

Satellite images can be integrated into a crop model to strengthen the advantages of each technique for crop monitoring and to compensate for weaknesses of each other, which can be systematically applied for monitoring inaccessible croplands. The objective of this study was to outline the productivity of paddy rice based on simulation of the yield of all paddy fields in North Korea, using a grid crop model combined with optical satellite imagery. The grid GRAMI-rice model was used to simulate paddy rice yields for inaccessible North Korea based on the bidirectional reflectance distribution function-adjusted vegetation indices (VIs) and the solar insolation. VIs and solar insolation for the model simulation were obtained from the Geostationary Ocean Color Imager (GOCI) and the Meteorological Imager (MI) sensors of the Communication Ocean and Meteorological Satellite (COMS). Reanalysis data of air temperature were achieved from the Korea Local Analysis and Prediction System (KLAPS). Study results showed that the yields of paddy rice were reproduced with a statistically significant range of accuracy. The regional characteristics of crops for all of the sites in North Korea were successfully defined into four clusters through a spatial analysis using the K-means clustering approach. The current study has demonstrated the potential effectiveness of characterization of crop productivity based on incorporation of a crop model with satellite images, which is a proven consistent technique for monitoring of crop productivity in inaccessible regions.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.331-331
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• 2022

This research aims to developing new commercialization project of convergence agricultural industrial model. First, we established an inventory for the planning of convergence agricultural industrial model categorize the relevant factors identified, and then suggested three models which are the business profit model for convergence agriculture industrialization, the resource recycling complex and agricultural tourism model, and the smart agricultural model. Second, in order to investigate the feasibility of each industrial model, we investigated the willingness to participate in the project according to the pilot models such as related organizations and management agencies, and proposed the result of business feasibility analysis. Finally, we suggested the establishment of a demonstration complex through the systemization of element technologies at two models. The related systems and technologies was reviewed as a new commercialization plan through the modeling of convergence agricultural industrial types in main crop production complex presented, and set up mid- to long-term development direction. The results of this study can be applied to the design of convergence agricultural industrial model in main crop production complex.

• Korean Journal of Remote Sensing
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• v.33 no.5_2
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• pp.617-620
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• 2017

The periodic monitoring of crop conditions and timely estimation of crop yield are of great importance for supporting agricultural decision-makings, as well as for effectively coping with food security issues. Remote sensing has been regarded as one of effective tools for crop condition monitoring and crop type classification. Since 2010, RDA (Rural Development Administration) has been developing technology for monitoring on crop condition using remote sensing and model. These special papers address recent state-of-the-art of remote sensing and geospatial technologies for providing operational agricultural information, such as, crop yield estimation methods using remote sensing data and process-oriented model, crop classification algorithm, monitoring and prediction of weather and climate based on remote sensing data,system design and architecture of crop monitoring system, history on rice yield forecasting method.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.53 no.2
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• pp.161-170
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• 2008

Interest is growing in applying simulation models for the South Texas conditions, to better assess crop water use and production with different crop management practices. The Environmental Policy Integrated Climate (EPIC) model was used to evaluate its application as a decision support tool for irrigation management of com (Zea mays L.) in South Texas of the U.S. We measured actual crop evapotranspiration (ETc) using a weighing lysimeter, soil moisture using a neutron probe, and grain yield by field sampling. The model was then validated using the measured data. Simulated ETc using the Hargreaves-Samani equation was in agreement with the lysimeter measured ETc. Simulated soil moisture generally matched with the measured soil moisture. The EPIC model simulated the variability in grain yield with different irrigation regimes with $r^2$value of 0.69 and root mean square error of $0.5\;ton\;ha^{-1}$. Simulation results with farm data demonstrate that EPIC can be used as a decision support tool for com under irrigated conditions in South Texas. EPIC appears to be effective in making long term and pre-season decisions for irrigation management of crops, while reference ET and phenologically based crop coefficients can be used for inseason irrigation management.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.107-114
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• 1999

The large-scale distribution of crops Is usually determined by climate. We present the results of a climate-crop prediction based on spatial bio-physical process model approach, implemented in a GIS (Geographic Information System) environment using several regional and global agriculture-environmental databases. The model utilizes daily climate data like temperature, rainfall, solar radiation being generated stocastically by in-built model weather generator to determine the daily biomass and finally the crop yield. Crops are characterized by their specific growing period requirements, photosynthesis, respiration properties and harvesting index properties. Temperature and radiation during the growing period controls the development of each crop. The model simulates geographic/spatial distribution of climate by which a crop-growing belt can also be determined. The model takes both irrigated and non-irrigated area crop productivity into account and the potential increase in productivity by the technical means like mechanization is not considered. All the management input given at the base year 1995 was kept same for the next twenty-year changes until 2015. The simulated distributions of crops under current climatic conditions coincide largely with the current agricultural or specific crop growing regions. Simulation with assumed weather generated derived climate change scenario illustrate changes in the agricultural potential. There are large regional differences in the response across the country. The north-south and east-west regions responded differently with projected climate changes with increased and decreased productivity depending upon the crops and scenarios separately. When water was limiting or facilitating as non-irrigated and irrigated area crop-production effects of temperature rise and higher $CO_2$ levels were different depending on the crops and accordingly their production. Rise in temperature led to yield reduction in case of maize and rice whereas a gain was observed for wheat crop, doubled $CO_2$ concentration enhanced yield for all crops and their several combinations behaved differently with increase or decrease in yields. Finally, with this spatial modeling approach we succeeded in quantifying the crop productivity which may bring regional disparities under the different climatic scenarios where one region may become better off and the other may go worse off.

• Korean Journal of Agricultural and Forest Meteorology
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• v.20 no.2
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• pp.228-232
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• 2018

Process-based crop models have been used to assess the impact of climate change on crop production. These models are implemented in procedural or object oriented computer programming languages including FORTRAN, C++, Delphi, Java, which have a stiff learning curve. The requirement for a high level of computer programming is one of barriers for efforts to develop and improve crop models based on biophysical process. In this study, we attempted to develop a Chinese cabbage model using Microsoft Excel with Visual Basic for Application (VBA), which would be easy enough for most agricultural scientists to develop a simple model for crop growth simulation. Results from Soil-Plant-Atmosphere-Research (SPAR) experiments under six temperature conditions were used to determine parameters of the Chinese cabbage model. During a plant growing season in SPAR chambers, numbers of leaves, leaf areas, growth rate of plants were measured six times. Leaf photosynthesis was also measured using LI-6400 Potable Photosynthesis System. Farquhar, von Caemmerer, and Berry (FvCB) model was used to simulate a leaf-level photosynthesis process. A sun/shade model was used to scale up to canopy-level photosynthesis. An Excel add-in, which is a small VBA program to assist crop modeling, was used to implement a Chinese cabbage model under the environment of Excel organizing all of equations into a single set of crop model. The model was able to simulate hourly changes in photosynthesis, growth rate, and other physiological variables using meteorological input data. Estimates and measurements of dry weight obtained from six SPAR chambers were linearly related ($R^2=0.985$). This result indicated that the Excel/VBA can be widely used for many crop scientists to develop crop models.