• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.374-390
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• 2021

Growth and yield of field vegetable crops would be affected by climate conditions, which cause a relatively large fluctuation in crop production and consumer price over years. The yield prediction system for these crops would support decision-making on policies to manage supply and demands. The objectives of this study were to compile literatures related to onion and garlic and to perform data-mining analysis, which would shed lights on the development of crop models for these major field vegetable crops in Korea. The literatures on crop growth and yield were collected from the databases operated by Research Information Sharing Service, National Science & Technology Information Service and SCOPUS. The keywords were chosen to retrieve research outcomes related to crop growth and yield of onion and garlic. These literatures were analyzed using text mining approaches including word cloud and semantic networks. It was found that the number of publications was considerably less for the field vegetable crops compared with rice. Still, specific patterns between previous research outcomes were identified using the text mining methods. For example, climate change and remote sensing were major topics of interest for growth and yield of onion and garlic. The impact of temperature and irrigation on crop growth was also assessed in the previous studies. It was also found that yield of onion and garlic would be affected by both environment and crop management conditions including sowing time, variety, seed treatment method, irrigation interval, fertilization amount and fertilizer composition. For meteorological conditions, temperature, precipitation, solar radiation and humidity were found to be the major factors in the literatures. These indicate that crop models need to take into account both environmental and crop management practices for reliable prediction of crop yield.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.89-89
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• 2022

Soybean (Glycine max (L.) Merr.) is a representative food resource. To preserve the integrity of soybean, it is necessary to protect soybean yield and seed quality from threats of various pests and diseases. Riptortus pedestris is a well-known insect pest that causes the greatest loss of soybean yield in South Korea. This pest not only directly reduces yields but also causes disorders and diseases in plant growth. Unfortunately, no resistant soybean resources have been reported. Therefore, it is necessary to identify the distribution and movement of Riptortus pedestris at an early stage to reduce the damage caused by insect pests. Conventionally, the human eye has performed the diagnosis of agronomic traits related to pest outbreaks. However, due to human vision's subjectivity and impermanence, it is time-consuming, requires the assistance of specialists, and is labor-intensive. Therefore, the responses and behavior patterns of Riptortus pedestris to the scent of mixture R were visualized with a 3D model through the perspective of artificial intelligence. The movement patterns of Riptortus pedestris was analyzed by using time-series image data. In addition, classification was performed through visual analysis based on a deep learning model. In the object tracking, implemented using the YOLO series model, the path of the movement of pests shows a negative reaction to a mixture Rina video scene. As a result of 3D modeling using the x, y, and z-axis of the tracked objects, 80% of the subjects showed behavioral patterns consistent with the treatment of mixture R. In addition, these studies are being conducted in the soybean field and it will be possible to preserve the yield of soybeans through the application of a pest control platform to the early stage of soybeans.

• Asian-Australasian Journal of Animal Sciences
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• v.30 no.8
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• pp.1074-1080
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• 2017

Objective: The growth, carcass and retail cut yield records on 1,428 Hanwoo steers obtained through progeny testing were analyzed in this study, and their heritability and genetic relationships among the traits were estimated using animal models. Methods: Two different models were compared in this study. Each model was fitted for different fixed class effects, date of slaughter for carcass traits and batch of progeny test live measurement traits, and a choice of covariates (carcass weight in Model 1 or backfat thickness in Model 2) for carcass traits. Results: The differences in body composition among individuals were deemed being unaffected by their age at slaughter, except for carcass weight and backfat thickness. Heritability estimates of body size measurements were 0.21 to 0.36. Heritability estimates of retail cut percentage were high (0.56 from Model 1 and 0.47 from Model 2). And the heritability estimates for loin muscle percentage were 0.36 from Model 1 and 0.42 from Model 2, which were high enough to consider direct selection on carcass cutability traits as effective. The genetic correlations between body size measurements and retail cut ratio (RCR) were close to zero. But, some negative genetic correlations were found with chest girths measured at yearling (Model 1) or at 24 months of age or with chest widths. Loin muscle ratio (LMR) was genetically negatively correlated with body weights or body size measurements, in general in Model 1. These relationships were low close to zero but positive in Model 2. Phenotypic correlation between cutability traits (RCR, LMR) and live body size measurements were moderate and negative in Model 1 while those in Model 2 were all close to zero. Conclusion: Therefore, the body weights or linear body measurements at an earlier age may not be the most desirable selection traits for exploitation of correlated responses to improve loin muscle or lean meat yield.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.398-403
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• 2023

Process-based K-cabbage model is based on physiological processes such as photosynthesis and phenology, making it possible to predict crop growth under different climate conditions that have never been experienced before. Current first-stage process-based models can be used to assess climate impact through yield prediction based on climate change scenarios, but no comparison has been performed between big data obtained from the main production area and model prediction so far. The aim of this study was to find out the direction of model improvement when using the current model for yield prediction. For this purpose, model performance evaluation was conducted based on data collected from farmers growing 'Chungwang' cabbage in Taebaek and Samcheok, the main producing areas of Chinese cabbage in highland region. The farms surveyed in this study had different cultivation methods in terms of planting date and soil water and nutrient management. The results showed that the potential biomass estimated using the K-cabbage model exceeded the observed values in all cases. Although predictions and observations at the time of harvest did not show a complete positive correlation due to limitations caused by the use of fresh weight in the model evaluation process (R²=0.74, RMSE=866.4), when fitting the model based on the values 2 weeks before harvest, the growth suitability index was different for each farm. These results are suggested to be due to differences in soil properties and management practices between farms. Therefore, to predict attainable yields taking into account differences in soil and management practices between farms, it is necessary to integrate dynamic soil nutrient and moisture modules into crop models, rather than using arbitrary growth suitability indices in current K-cabbage model.

• The Korean Journal of Applied Statistics
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• v.36 no.6
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• pp.619-629
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• 2023

Fertilizers are crucial for increasing crop yield, but using too much of them without taking into account the nutrients that the crops need can increase costs for farm management and have a negative impact on the environment. Through smart agriculture, fertilizers can be applied as needed at the right time to reflect the growth characteristics of crops, reducing the burden of fertilizer losses and providing economical nutrient management. In this study, we use the total dry weight of field-cultivated red pepper and green onion grown in various growing environments to fit a nonlinear model-based crop growth model using different growth curves (logistic, Gompertz, Richards, and double logistic curve), and we propose a fertilizer distributed algorithm based on crop growth rate.

• Journal of Korean Society of Forest Science
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• v.90 no.5
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• pp.650-662
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• 2001

The growth and yield models for five different kinds of natural forest types were systemically developed in the natural Broadleaved-Korean pine Forests in Northeast China. The data were collected from 359 temporary plots and 58 permanent plots with area ranged from 0.06 ha to 1.0 ha, ranging in stand age from 43 to 364 years. The Site Class Index (SCI) was introduced to evaluate site quality and the Crown Competition Factor (CCF) was selected as a measure of stand density for the mixed natural forest. The Chapman-Richards function was adopted to develop SCI equation and height-diameter curve. The Schumacher growth function was selected as base model to develop the DBH, basal area, and stand volume growth models by using re-parameterized method. In modeling mean DBH and basal area growth, it was found that the asymptotic parameter A of Schumacher function was exponentially related to site quality (SCI) and stand density (CCF). The rate parameter k was related to stand density and it was independent of SCI. Several validation measures for predicted stand variables were evaluated in the growth and yield models using independent data sets. The results indicated that relative mean errors (RME) in predicted stand attributes were less than ${\pm}5%$ and the estimated precision values of the stand variables were all greater than 95%.

• The Journal of Fisheries Business Administration
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• v.50 no.1
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• pp.17-37
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• 2019

The purpose of this study is to estimate the resource recovery effect and the economic effect of the fishermen by the fisheries vessel buy-back program. First, this study standardizes the fishing efforts of coastal gill net, coastal trap, and coastal composite fisheries using Gavaris general linear model. Second, the resource evaluation is performed by using vessel buy-back program data, and also the CYP model based on exponential growth function is applied. In order to derive the effect of the vessel buy-back program, the MSY with the vessel buy-back program is compared with the MSY without the vessel buy-back program. Finally, we compare and analyze producer surplus under the equilibrium of the MEY and the OA using bioeconomic model. In conclusion, the vessel buy-back program has shown an increase in resource growth and economic improvement for the remaining fishermen. The result shows that the remaining fishermen are able to obtain an increase in producer surplus of about 53% due to the vessel buy-back program under equilibrium levels of the open access and the maximum economic yield.

• Journal of Korean Society of Forest Science
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• v.57 no.1
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• pp.1-7
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• 1982

The simple logistic growth model on the logistic curve, being originally a kind of population growth curve has also been sometimes utilized to describe growth curves in herbaceous plants such as duckweed and sun-flowers. It has already been recognized that the agreement between the theoretical calculations and the empirical observations is quite satisfactory form a practical point of view. It remains, however, still doubtful whether the logistic curve could be applied to the growth or ordinary woody plants which is quite different in its character from that of herbaceous plants. In this study, the simple logistic model, being a basic tool of stand density management, is applied to yield data from pine stands in order to test the adequacy of the model An attempt of testing the significance of the fit is made by applying the Chi-square test.

• Journal of Korean Society of Forest Science
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• v.80 no.4
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• pp.427-435
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• 1991

Linear programming(LP) is a well-known method in optimizing timber harvest schedules. This paper describes a linear programming formulation of korean pine stands for timber harvest scheduling problems. Simulation technique and LP were applied to optimize the time and space distribution of the sustained yield for the 10-year forest management planning horizon. Growthfunction of korean pine stands in study area was derived with the yield table. This growthfunction was contained to the simulation model in estimating of changing stand volume conditions for the planning horizon. These estimated values were served as the basic data of LP model, and LP model was formulated with the maximum of periodical harvest volume calculated by the classical yield regulation method (Paulsen-Hundeshagen formula) and the maximum of periodical harvest area calculated for the normal age distribution. The timber harvest schedule was established periodically for each subcompartment of korean pine stands in experiment forest of College of Forestry in Kangweon National University with the here developed LP model.

• The Journal of Fisheries Business Administration
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• v.47 no.3
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• pp.15-33
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• 2016

This study aims to analyze effectiveness of the resource use under the total allowable catch system (TACs) of Comb pen shell, a species among TAC targeting ones through its stock assessment based on the surplus production model such as the Clark Yoshimoto Pooley (CYP) model. Particularly, this study is separated into five analysis periods in order to understand changes in Comb pen shell resource and its efficient use after TAC system implemented in 2001. The results of this study are as follows. First, five sustainable yield curves (SYCs) and exponential growth functions (EGFs) produced by the surplus production model based on Gompertz growth function to compare before and after implementation of the Korean TAC system show that the TAC system has generated a positive stock rebuilding effect for Comb pen shell caught by the diver fishery since 2001. Secondly, five profits based on differences between the sustainable total revenue (STR) and the total cost (TC) with respect to fishing efforts present that the TAC system has increased efficiency of resource use of Comb pen shell caught by the diver fishery after implementation of the Korean TAC system. In conclusion, the Korean TAC system has increased efficiency of resource use as well as has led a positive stock rebuilding effect for Comb pen shell.