• Asian-Australasian Journal of Animal Sciences
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• v.13 no.5
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• pp.575-579
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• 2000

First lactation records of 683 Murrah buffaloes maintained at NDRI, Karnal which were progeny of 84 sires used for comparing the heritability estimates of age at first calving, first lactation milk yield and first service period under single and multiple trait models using restricted maximum likelihood (REML) method of estimation under an individual animal model. The results indicated that the heritability estimates may vary under single and multiple trait models depending upon the magnitude of genetic and environmental correlation among the traits being considered. Therefore, a single or multiple trait model is recommended for estimation of variance components depending upon the goal of breeding programme. However, there may not be any advantage of considering a trait with zero or near zero heritability and having no or very low genetic correlation with other traits in the model. Lower heritability estimates of part lactation yield (120-day milk yield) implied that there may not be any advantage of considering this trait in place of actual 305-day milk yield, whereas, comparable heritability estimates of predicted 305-day milk yield suggested that it could be used for sire evaluation to reduce the cost of milk recording under field conditions.

• Asian-Australasian Journal of Animal Sciences
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• v.8 no.4
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• pp.365-368
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• 1995

Lactation curves of dairy cows were generated using three models, namely; incomplete gamma function (model 1), polynomial inverse function (model 2) and non-linear regression (model 3). Secondary milk yield data of 27 cows which had completed 6 lactations were used in this study. Milk yield records (once a week) throughout the lactation and from the first three months of lactation were fitted to the models. Estimation of total milk yield by model 3 using the data once a week throughout the lactation resulted in smaller % bias and standard error than those generated from model 1 and 2. But, model 2 was more accurate in predicting the 305-day milk yield equivalent closer to actual yields with smaller bias % and error using partial records up to 3 months. Also, model 2 was able to estimate the time to reach peak yield close to the actual data using partial records and model 2 could be used as a tool to advise farmers on appropriate feeding and management practices to be adopted.

• 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.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.4
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• pp.383-390
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• 2016

Remote sensing data has been widely used in the estimation of crop yields by employing statistical methods such as regression model. Machine learning, which is an efficient empirical method for classification and prediction, is another approach to crop yield estimation. This paper described the corn yield estimation in Iowa State using four machine learning approaches such as SVM (Support Vector Machine), RF (Random Forest), ERT (Extremely Randomized Trees) and DL (Deep Learning). Also, comparisons of the validation statistics among them were presented. To examine the seasonal sensitivities of the corn yields, three period groups were set up: (1) MJJAS (May to September), (2) JA (July and August) and (3) OC (optimal combination of month). In overall, the DL method showed the highest accuracies in terms of the correlation coefficient for the three period groups. The accuracies were relatively favorable in the OC group, which indicates the optimal combination of month can be significant in statistical modeling of crop yields. The differences between our predictions and USDA (United States Department of Agriculture) statistics were about 6-8 %, which shows the machine learning approaches can be a viable option for crop yield modeling. In particular, the DL showed more stable results by overcoming the overfitting problem of generic machine learning methods.

• Proceedings of the Korean Society of Crop Science Conference
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• 1998.04a
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• pp.101-102
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• 1998

• Korean Journal of Soil Science and Fertilizer
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• v.47 no.2
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• pp.92-99
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• 2014

The crop growing conditions make accurate predictions of yield ahead of harvest time difficult. Such predictions are needed by the government to estimate, ahead of time, the amount of crop required to be imported to meet the expected domestic shortfall. Corn and soybean especially are widely cultivated throughout the world and a staple food in many regions of the world. On the other hand, the CASA (Carnegie-Ames-Stanford Approach) model is a process-based model to estimate the land plant NPP (Net Primary Productivity) based on the plant growing mechanism. In this paper, therefore, a methodology for the estimation of corn/soybean yield ahead of harvest time is developed specifically for the growing conditions particular to Iowa and Illinois. The method is based on CASA model using MODIS data, and uses Net Primary Productivity (NPP) to predict corn/soybean yield. As a result, NPP at DOY 217 (in Illinois) and DOY 241 (in Iowa) tend to have high correlation with corn/soybean yields. The corn/soybean yields of Iowa in 2013 was estimated to be 11.24/3.55 ton/ha and Illinois was estimated to be 10.09/3.06 ton/ha. Errors were 6.06/17.58% and -10.64/-7.07%, respectively, compared with the yield forecast of the USDA. Crop yield distributions in 2013 were presented to show spatial variability in the state. This leads to the conclusion that NPP changes in the crop field were well reflected crop yield in this study.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.4
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• pp.1-9
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• 2012

Decisions on reliability screening rules and burn-in policies are determined based on the estimated reliability. The variability in a semiconductor manufacturing process does not only causes quality problems but it also makes reliability estimation more complicated. This study investigates the nonuniformity characteristics of integrated circuit reliability according to defect density distribution within a wafer and between wafers then develops optimal burn-in policy based on the estimated reliability. New reliability estimation model based on yield information is developed using a spatial stochastic process. Spatial defect density variation is reflected in the reliability estimation, and the defect densities of each die location are considered as input variables of the burn-in optimization. Reliability screening and optimal burn-in policy subject to the burn-in cost minimization is examined, and numerical experiments are conducted.

• Journal of Korean Society of Steel Construction
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• v.10 no.3 s.36
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• pp.525-536
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• 1998

This paper is concerned with a theoretical study on the local load-carrying capacities of Concrete-Filled Circular Tubular(CFCT) column to H-beam connections by yield line theory. In this paper, the three cases which are assumed the yield line are involved. The first model is a simplified yield line model. The second model is modified by x and kx factors. The last one is a Morita's model. The local load-carrying capacities of CFCT column to H-beam connections has been studied both experimentally and theoretically using the yield line theory. The purpose of this paper is to suggest the basic data for developing the non-diaphragm connection.

• Korean Journal of Organic Agriculture
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• v.27 no.2
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• pp.225-235
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• 2019

The present study hypothesized that ratio between carcass traits components could be applied for the understanding of yield index in Hanwoo steer. A thousand data was generated based on average carcass weight (CW), loin area (LA) and backfat thickness (BT) of Hanwoo steer in December 2018 for analysis 1. Then yield index (YI) was calculated using newly established yield index equation. The correlation between yield index and each carcass traits was visualized. In the interaction between carcass traits components (LA, CW, BT) and YI, only the interactions including BT showed a regular pattern to YI. Then changes of YI according to ratio of carcass traits components were investigated. The observed interactions between LABT and CWBT were similar with Monod equation model. The changes of YI to LABT and CWBT were fitted to Monod equation, and yield constants (K1 for LABT; K2, CWBT) of each equation were calculated as 0.47 and 2.20, respectively. Carcass traits from 5 commercial Hanwoo steer farm were then employed in the second analysis. Yield constants of each farm were estimated. In estimation, R² value for K1 (LABT) showed greater than the K2 (CWBT). Finally, each farm was plotted based on their K1 and K2 values and it was found that greater yield index of Hanwoo steer was found as increased K1 and K2. As conclusion, the present study suggested the possibility of K1 and K2 values for understanding of yield grade equation and their application in the evaluation of new model for yield grade estimation and feeding strategy.

• Economic Analysis
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• v.26 no.1
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• pp.84-132
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• 2020

A central bank infers market expectations of future yields based on yield curves. The central bank needs to precisely understand the changes in market expectations of future yields in order to have a more effective monetary policy. This need explains why a range of models have attempted to produce yield curves and market expectations that are as accurate as possible. Alongside the development of bond markets, the interconnectedness between them and macroeconomic factors has deepened, and this has rendered understanding of what macroeconomic variables affect yield curves even more important. However, the existence of various theories about determinants of yields inevitably means that previous studies have applied different macroeconomics variables when estimating yield curves. This indicates model uncertainties and naturally poses a question: Which model better estimates yield curves? Put differently, which variables should be applied to better estimate yield curves? This study employs the Dynamic Nelson-Siegel Model and takes the Bayesian approach to variable selection in order to ensure precision in estimating yield curves and market expectations of future yields. Bayesian variable selection may be an effective estimation method because it is expected to alleviate problems arising from a priori selection of the key variables comprising a model, and because it is a comprehensive approach that efficiently reflects model uncertainties in estimations. A comparison of Bayesian variable selection with the models of previous studies finds that the question of which macroeconomic variables are applied to a model has considerable impact on market expectations of future yields. This shows that model uncertainties exert great influence on the resultant estimates, and that it is reasonable to reflect model uncertainties in the estimation. Those implications are underscored by the superior forecasting performance of Bayesian variable selection models over those models used in previous studies. Therefore, the use of a Bayesian variable selection model is advisable in estimating yield curves and market expectations of yield curves with greater exactitude in consideration of the impact of model uncertainties on the estimation.