• Korean Journal of Organic Agriculture
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• v.29 no.3
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• pp.317-332
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• 2021

This study estimated the predictive onion yield through Stepwise regression of big data and weather variables by onion growing season. The economic feasibility of onion observations using big data was analyzed using estimated predictive data. The social welfare effect was estimated through the model of Harberger's triangle using onion yield prediction with big data and it without big data. Predicted yield using big data showed a deviation of -9.0% to 4.2%. As a result of estimating the social welfare effect, the average annual value was 23.3 billion won. The average annual value of social welfare effects if big data was not used was measured at 22.4 billion won. Therefore, it was estimated that the difference between the social welfare effect when the prediction using big data was used and when it was not was about 950 million won. When these results are applied to items other than onion items, the effect will be greater. It is judged that it can be used as basic data to prove the justification of the agricultural observation project. However, since the simple Harberger's triangle theory has the limitation of oversimplifying reality, it is necessary to evaluate the economic value through various methods such as measuring the effect of agricultural observation under a more realistic rational expectation hypothesis in future studies.

• Economic Analysis
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• v.26 no.3
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• pp.1-47
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• 2020

This paper surveys research since the 1990s on the ability of the yield spread and its components (i.e., expectation spread and term premium components) for future economic activity, and also conducts an empirical analysis of their forecasting ability using the yield data of Korean government bonds. This paper's survey, particularly for the US, shows that the yield spread has significant predictive power for some macroeconomic variables, but since the mid-1980s, its predictive power seems to have declined, possibly due to stronger inflation targeting. Next, this paper's empirical analysis using Korean data indicates that the yield spread, and the term premium component in particular, has significant predictive power for industrial production (IP) growth, consumer price index growth, and the IP gap. An out-of-sample analysis shows that the prediction equations are unstable over time, and that in predicting IP growth, the yield spread decomposition makes a significant contribution to the prediction of IP growth.

• Animal Bioscience
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• v.34 no.4
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• pp.770-782
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• 2021

Objective: The aim of the present study was to compare the effectiveness of three approaches (the seasonal auto-regressive integrated moving average [SARIMA] model, the nonlinear autoregressive exogenous [NARX] artificial neural networks and Wood's model) to the prediction of milk yield during lactation. Methods: The dataset comprised monthly test-day records from 965 Polish Holstein-Friesian Black-and-White primiparous cows. The milk yields from cows in their first lactation (from 5 to 305 days in milk) were used. Each lactation was divided into ten lactation stages of approximately 30 days. Two age groups and four calving seasons were distinguished. The records collected between 2009 and 2015 were used for model fitting and those from 2016 for the verification of predictive performance. Results: No significant differences between the predicted and the real values were found. The predictions generated by SARIMA were slightly more accurate, although they did not differ significantly from those produced by the NARX and Wood's models. SARIMA had a slightly better performance, especially in the initial periods, whereas the NARX and Wood's models in the later ones. Conclusion: The use of SARIMA was more time-consuming than that of NARX and Wood's model. The application of the SARIMA, NARX and Wood's models (after their implementation in a user-friendly software) may allow farmers to estimate milk yield of cows that begin production for the first time.

• Journal of The Korean Society of Grassland and Forage Science
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• v.41 no.4
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• pp.287-294
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• 2021

The objective of this study was conducted to calculate the damage of whole crop maize in accordance with abnormal climate using the forage yield prediction model through machine learning. The forage yield prediction model was developed through 8 machine learning by processing after collecting whole crop maize and climate data, and the experimental area was selected as Gyeonggi-do. The forage yield prediction model was developed using the DeepCrossing (R²=0.5442, RMSE=0.1769) technique of the highest accuracy among machine learning techniques. The damage was calculated as the difference between the predicted dry matter yield of normal and abnormal climate. In normal climate, the predicted dry matter yield varies depending on the region, it was found in the range of 15,003~17,517 kg/ha. In abnormal temperature, precipitation, and wind speed, the predicted dry matter yield differed according to region and abnormal climate level, and ranged from 14,947 to 17,571, 14,986 to 17,525, and 14,920 to 17,557 kg/ha, respectively. In abnormal temperature, precipitation, and wind speed, the damage was in the range of -68 to 89 kg/ha, -17 to 17 kg/ha, and -112 to 121 kg/ha, respectively, which could not be judged as damage. In order to accurately calculate the damage of whole crop maize need to increase the number of abnormal climate data used in the forage yield prediction model.

• Journal of The Korean Society of Grassland and Forage Science
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• v.37 no.2
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• pp.145-153
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• 2017

This study was aimed to find yield prediction model of Italian ryegrass using climate big data and geographic information. After that, mapping the predicted yield results using Geographic Information System (GIS) as follows; First, forage data were collected; second, the climate information, which was matched with forage data according to year and location, was gathered from the Korean Metrology Administration (KMA) as big data; third, the climate layers used for GIS were constructed; fourth, the yield prediction equation was estimated for the climate layers. Finally, the prediction model was evaluated in aspect of fitness and accuracy. As a result, the fitness of the model ($R^2$) was between 27% to 95% in relation to cultivated locations. In Suwon (n=321), the model was; DMY = 158.63AGD -8.82AAT +169.09SGD - 8.03SAT +184.59SRD -13,352.24 (DMY: Dry Matter Yield, AGD: Autumnal Growing Days, SGD: Spring Growing Days, SAT: Spring Accumulated Temperature, SRD: Spring Rainfall Days). Furthermore, DMY was predicted as $9,790{\pm}120$ (kg/ha) for the mean DMY(9,790 kg/ha). During mapping, the yield of inland areas were relatively greater than that of coastal areas except of Jeju Island, furthermore, northeastern areas, which was mountainous, had lain no cultivations due to weak cold tolerance. In this study, even though the yield prediction modeling and mapping were only performed in several particular locations limited to the data situation as a startup research in the Republic of Korea.

• Journal of the Korean Society of Tobacco Science
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• v.11 no.2
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• pp.115-126
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• 1989

This study was conducted to determine the time and methods of predicting tobacco yield, by studying the relationship of yield components to yield. 1. The relationship between each position in leaf dry weight and approached gradually each other and also correlation coefficient of top leaf was higher than that of lower leaf. The leaf dry weight per plant was highly correlated with leaf area from 16th leaf position on stalk. Leaf dry weight of each leaf position on stalk was highly correlated with leaf dry weight per plant at 14 to 16th leaf position. 2. The correlation coefficient between leaf dry weight and leaf area per plant was higher at the late growth stage than at the early growth stage, and higher between the near stages. Correlation coefficient between leaf dry weights was higher than that of leaf areas. 3. Flue-cured tobacco yield be estimated from leaf dry weight per plant at 50 to 55 days after transplanting. 4. Air-cured tobacco yield could be predicted from leaf dry weight per plant at 60 days after transplanting.

• Korean Journal of Agricultural Science
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• v.47 no.4
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• pp.803-814
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• 2020

Managing erosion control dams requires the annual average sediment yield to determine their storage capacity and time to full sediment-fill and dredging. The GeoWEPP (Geo-spatial interface for Water Erosion Prediction Project) model can predict the annual average sediment yield from various land uses and vegetation covers at a watershed scale. This study assessed the GeoWEPP to determine the annual average sediment yield for managing erosion control dams by applying it to five erosion control dams and comparing the results with field observations using ground-based LiDAR (light detection and ranging). The modeling results showed some differences with the observed sediment yields. Therefore, GeoWEPP is not recommended to determine the annual average sediment yield for erosion control dams. Moreover, when using the GeoWEPP, the following is recommended :1) use the US WEPP climate files with similar latitude, elevation and precipitation modified with monthly average climate data in Korea and 2) use soil files based on forest soil maps in Korea. These methods resulted in GeoWEPP predictions and field observations of 0 and 63.3 Mg·yr-1 for the Gangneung, 142.3 and 331.2 Mg·yr-1 for the Bonghwa landslide, 102.0 and 107.8 Mg·yr-1 for the Bonghwa control, 294.7 and 115.0 Mg·yr-1 for the Chilgok forest fire, and 0 and 15.0 Mg·yr-1 for the Chilgok control watersheds. Application of the GeoWEPP in Korea requires 1) building a climate database fit for the WEPP using the meteorological data from Korea and 2) performing further studies on soil and streamside erosion to determine accurate parameter values for Korea.

• Journal of information and communication convergence engineering
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• v.21 no.1
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• pp.82-89
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• 2023

Various machine-learning models may yield high predictive power for massive time series for time series prediction. However, these models are prone to instability in terms of computational cost because of the high dimensionality of the feature space and nonoptimized hyperparameter settings. Considering the potential risk that model training with a high-dimensional feature set can be time-consuming, we evaluate a feature-importance-based feature selection method to derive a tradeoff between predictive power and computational cost for time series prediction. We used two machine learning techniques for performance evaluation to generate prediction models from a retail sales dataset. First, we ranked the features using impurity- and Local Interpretable Model-agnostic Explanations (LIME) -based feature importance measures in the prediction models. Then, the recursive feature elimination method was applied to eliminate unimportant features sequentially. Consequently, we obtained a subset of features that could lead to reduced model training time while preserving acceptable model performance.

• Journal of Animal Science and Technology
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• v.45 no.2
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• pp.335-342
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• 2003

This study was conducted to predict the carcass yield traits using ultrasound before slaughter and to enhance the prediction accuracy of carcass yield grade by applying various strategies. For this experiment, five hundred seventy three Hanwoo steers of 24 months of age were used. Difference between ultrasound result and carcass measure of BFT and LMA was 0.6$\pm$1.65mm and 0.7$\pm$5.56cm2, respectively. Correlation coefficient between ultrasound result and carcass measure of BFT and LMA was 0.86 and 0.82, respectively (p<0.001). Results for improving predictions of yield grade by four methods-the Korean yield grade index equation, fat depth alone, regression and decision tree methods were 80.3%, 81.3%, 80.1% and 81.8%, respectively. We conclude that the decision tree method can easily predict yield grade and is also useful for increasing prediction accuracy rate.

• Journal of Korean Society of Forest Science
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• v.112 no.2
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• pp.209-216
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• 2023

This study aimed to use national forest inventory data to develop a forest productivity index and yield prediction model of a Tilia amurensis stand. The site index displaying the forest productivity of the Tilia amurensis stand was developed as a Schumacher model, and the site index classification curve was generated from the model results; its distribution growth in Korea ranged from 8-16. The growth model using age as an independent variable for breast height and height diameter estimation was derived from the Chapman-Richards and Weibull model. The Fitness Indices of the estimation models were 0.32 and 0.11, respectively, which were generally low values, but the estimation-equation residuals were evenly distributed around 0, so we judged that there would be no issue in applying the equation. The stand basal area and site index of the Tilia amurensis stand had the greatest effect on the stand-volume change. These two factors were used to derive the Tilia amurensis stand yield model, and the model's determination coefficient was approximately 94%. After verifying the residual normality of the equation and autocorrelation of the growth factors in the yield model, no particular problems were observed. Finally, the growth and yield models of the Tilia amurensis stand were used to produce the makeshift stand yield table. According to this table, when the Tilia amurensis stand is 70 years old, the estimated stand-volume per hectare would be approximately 208 m³ . It is expected that these study results will be helpful for decision-making of Tilia amurensis stands management, which have high value as a forest resource for honey and timber.