• Asian-Australasian Journal of Animal Sciences
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• v.11 no.5
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• pp.580-585
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• 1998

The study was conducted to determine if variation in protein yield can be explained by expressions of early lactation somatic cell score (SCS) and if prediction can be improved by including SCS among the predictors. A data set was prepared (n = 663,438) from Wisconsin Dairy Improvement Association (USA) records for protein yield with sample days near 20. Stepwise regression was used requiring F statistic (p < .01) for any variable to stay in the model. Separate analyses were run for 12 combinations of four seasons and first three parities. Selection of SCS variables was not consistent across seasons or lactations. Coefficients of detennination ($R^2$) ranged from 51 to 61% with higher values for earlier lactations. Including any expression of SCS in the prediction equations improved $R^2$ by < 1 %. SCS was associated with milk yield on the sample day, but the association was not strong enough to improve the prediction of future yield when other expressions of milk yield were in the model.

• Korean Journal of Agricultural Science
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• v.41 no.4
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• pp.481-485
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• 2014

According to statistical data the past ten years, cultivation area and yield of radish are steadily decreasing. This phenomenon cause instability of radish's supply due to meteorological chage, even if radish's yield per unit area is increasing by cultivation technological development. These problems raise radish's price. So, we conducted study on meteorological factors for accuracy improvement of radish yield estimation. Panel analysis was used with two-way effect model considering group effect and time effect. As the result, we show that mixed effects model (fixed effect: group, random effects: time) was statistical significance. According to the model, a rise of one degree in the average air temperature on August will decrease radish's yield per unit area by $428kg{\cdot}10a^{-1}$ and that in the average air temperature on October will increase radish's yield per unit area by $438kg{\cdot}10a^{-1}$. The reason is that radish's growth will be easily influenced by meteorological condition of a high temperature on August and by meteorological condition of a low temperature on Octoboer.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.106-106
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• 2018

Ecosystem service valuation is a crucial step for the sustainable management of watershed. In the context of various ecosystem services provided by watershed, this study, particularly deals with water yield computation in Bagmati Basin of Nepal. The water availability per population in Bagmati Basin is lowest compared to other basins in Nepal. Also, the rate of urbanization is rapidly growing over a decade. In this regard, the objectives of this study are 1) to compute the total water yield of the basin along with computation on a sub-watershed scale, and 2) Study the impacts of land use change on water yield based on CLUE-S model. For the study, Integrated Valuation of Environmental Services and Tradeoffs (InVEST), a popular model for ecosystem service assessment based on Budyko hydrological method is used to compute water yield. As well, CLUE-S model is used to study land use change, which is further related to study variation on water yield. The sub-watershed wise outcome of the study is expected to provide the guidelines for the effective and economic management of a watershed on a regional scale.

• Journal of the Korean Society for Advanced Composite Structures
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• v.5 no.4
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• pp.1-10
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• 2014

A progressive failure analysis procedure for composite laminates is developed in here and in the companion paper. An anisotropic plastic constitutive model for fiber-reinforced composite material, is developed, which is simple and efficient to be implemented into computer program for a predictive analysis procedure of composites. In current development of the constitutive model, an incremental elastic-plastic constitutive model is adopted to represent progressively the nonlinear material behavior of composite materials until a material failure is predicted. An anisotropic initial yield criterion is established that includes the effects of different yield strengths in each material direction, and between tension and compression. Anisotropic work-hardening model and subsequent yield surface are developed to describe material behavior beyond the initial yield under the general loading condition. The current model is implemented into a computer code, which is Predictive Analysis for Composite Structures (PACS), and is presented in the companion paper. The accuracy and efficiency of the anisotropic plastic constitutive model are verified by solving a number of various fiber-reinforced composite laminates with and without geometric discontinuity. The comparisons of the numerical results to the experimental and other numerical results available in the literature indicate the validity and efficiency of the developed model.

• Steel and Composite Structures
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• v.38 no.2
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• pp.151-164
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• 2021

During the pipe forming process, a steel plate undergoes inelastic behavior multiple times under a load condition repeating tension and compression in the circumferential direction. It derives local reduction or increase of yield strength within the thickness of steel pipes by the plastic hardening and Bauschinger effect. In this study, a combined hardening model is proposed to effectively predict variations of yield strength in the circumferential direction of API-X65 and X70 steel pipes with relatively low t/D ratio during the forming process, which is expected to experience accumulated plastic strain of 2~3%, the typical Lüder band range in a low-carbon steel. Cyclic tensile tests of API-X65 and X70 steels were performed, and the parameters of the proposed model for the steels were calibrated using the test results. Bending-flattening tests to simulate repeated tension and compression during pipe forming were followed for API-X65 and X70 steels, and the results were compared with those by the proposed model and Zou et al. (2016), in order to verify the process of material model calibration based on tension-compression cyclic test, and the accuracy of the proposed model. Finally, parametric analysis for the yield strength of the steel plate in the circumferential direction of UOE pipe was conducted to investigate the effects of t/D and expansion ratios after O-forming on the yield strength. The results confirmed that the model by Zou et al. (2016) underestimated the yield strength of steel pipe with relatively low t/D ratio, and the parametric analysis showed that the t/D and expansion ratio have a significant impact on the strength of steel pipe.

• Transactions of Materials Processing
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• v.19 no.7
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• pp.393-398
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• 2010

This paper investigates the effect of material properties on the formability of sheet metals based on the Marciniak-Kuczynski model (M-K model). The hardening behavior of the material is modeled as the Hollomon model with the strain rate effect. The yield surfaces are constructed with Hosford79 yield function. The material properties considered in this study include the R-value, the strain hardening exponent, the strain rate hardening exponent, and the crystal structure of the material. The effect of the crystal structure on formability is roughly expressed as the change of the yield surface by varying the value of the exponent in Hosford79 yield function. Results show that the R-value affects neither the magnitude nor the shape of right hand side of forming limit diagrams (FLDs). Higher strain hardening exponent and higher strain rate hardening exponent improve the formability of sheet metals because they stabilize the forming processes.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.207-209
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• 2003

Quantifying crop production is one of the most important applications of remote sensing in which the temporal and up-to-date data can play very important role in avoiding any immediate insufficiency in agricultural production. A combination of climatic data and biophysical parameters derived from Landsat7 ETM+ was used to develop a mathematical model for wheat yield forecast in different geographically wide Wheat growing districts in Egypt. Leaf Area Index (LAI) and fraction of Absorbed Photosynthetically Active Radiation (fAPAR) with temperature were used in the modeling. The model includes three sub-models representing the correlation between the reported yield and each individual variable. Simulation results using district statistics showed high accuracy of the derived correlations to estimate wheat production with a percentage standard error (%S.E.) of 1.5% in El- Qualyobia district and average (%S.E.) of 7% for the whole wheat areas.

• Computers and Concrete
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• v.17 no.2
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• pp.157-172
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• 2016

This paper presents a Strut-and-Tie Model for reinforced concrete (RC) columns subject to lateral loading. The proposed model is based on the loading path for the post-yield state, and the geometries of struts and tie are determined by the stress field of post-yield state. The analysis procedure of the Strut-and-Tie Model is that 1) the shear force and displacement at the initial yield state are calculated and 2) the relationship between the additional shear force and the deformation is determined by modifying the geometry of the longitudinal strut until the ultimate limit state. To validate the developed model, the ultimate strength and associated deformation obtained by experimental results are compared with the values predicted by the model. Good agreements between the proposed model and the experimental data are observed.

• 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 The Korean Society of Grassland and Forage Science
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• v.41 no.1
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• pp.47-55
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• 2021

The objective of this study was to access the effect of climate and soil factors on alfalfa dry matter yield (DMY) by the contribution through constructing the yield prediction model in a general linear model considering climate and soil physical variables. The processes of constructing the yield prediction model for alfalfa was performed in sequence of data collection of alfalfa yield, meteorological and soil, preparation, statistical analysis, and model construction. The alfalfa yield prediction model used a multiple regression analysis to select the climate variables which are quantitative data and a general linear model considering the selected climate variables and soil physical variables which are qualitative data. As a result, the growth degree days(GDD) and growing days(GD), and the clay content(CC) were selected as the climate and soil physical variables that affect alfalfa DMY, respectively. The contributions of climate and soil factors affecting alfalfa DMY were 32% (GDD, 21%, GD 11%) and 63%, respectively. Therefore, this study indicates that the soil factor more contributes to alfalfa DMY than climate factor. However, for examming the correct contribution, the factors such as other climate and soil factors, and the cultivation technology factors which were not treated in this study should be considered as a factor in the model for future study.