• KOREAN JOURNAL OF CROP SCIENCE
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• v.3
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• pp.89-98
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• 1965

The experimental studies were intended to clarify the effects of selection, and also aimed at estimating the heritabilities, the genotypic correlations among some agronomic characters, and at calculating the selection index on some selective characters for the selection of desirable lines, under different climatic conditions. Finally practical implications of these studies, especially on the selection index, were discussed. Twenty-two varieties, determinate growing habit type, were selected at random from the 138 soybean varieties cultivated the year before, were grown in a randomized block design with three replicates at Chinju, Korea, under May and June sowing conditions. The method of estimating heritabilities for the eleven agronomic characters-flowering date, maturity date, stem length, branch numbers per plant, stem diameter, plant weight, pod numbers per plant, grain numbers per plant and 100 grain weight, shown in Table 3, was the variance components procedures in a replicated trial for the varieties. The analysis of covariance was used to obtain the genotypic correlations and phenotypic correlations among the eight characters, and the selection indexes for some agronomic characters were calculated by Robinson's method. The results are summarized as follows: Heritabilities : The experiment on the genotype-environment interaction revealed that in almost all of the characters investigated the interaction was too large to be neglected and materially affected the estimates of various genotypic parameters. The variation in heritability due to the change of environments was larger in the characters of low heritability than in those of high heritability. Heritability values of flowering date, fruiting period (days from flowering to maturity), stem length and 100 grain weight were the highest in both environments, those of yield(grain weight) and other characters were showed the lower values(Table 3). These heritability values showed a decreasing trend with the delayed sowing in the experiments. Further, all calculated heritability values were higher than anticipated. This was expected since these values, which were the broad sense heritability, contain the variance due to dominance and epistasisf in addition to the additive genetic variance. Genotypic correlations : Genotypic correlations were slightly higher than the corresponding phenotypic correlations in both environments, but the variation in values due to the change of environment appeared between grain weight and some other characters, especially an increase between grain weight and flowering date, and the total growing period(Table 6). Genotypic correlations between grain weight and other characters indicated that high seed yield was genetically correlated with late flowering, late maturity, and the other five characters namely branch numbers per plant, stem diameter, plant weight, pod numbers per plant and grain numbers per plant, but not with 100 grain weight of soybeans. Pod numbers and grain numbers per plant were more closely correlated with seed yields than with other characters. Selection index : For the comparison and the use of selection indexes in the selection, two kinds of selection indexes were calculated, the former was called selection index A and the later selection index B as shown in Table 7. Selection index A was calculated by the values of grain weight per plant as the character of yield(character Y), but the other, selection index B, was calculated by the values of pod numbers per plant, instead of grain weight per plant, as the character of yield'(character Y'). These results suggest that selection index technique is useful in soybean breeding. In reality, however, as the selection index varies with population and environment, it must be calculated in each population to which selection is applied and in each environment in which the population is located. In spite of the expected usefulness of selection index technique in soybean breeding, unsolved problems such as the expense, time and labor involved in calculating the selection index remain. For these reasons and from these experimental studies, it was recognized that in the breeding of self-fertilized soybean plants the selection for yield should be based on a more simple selection index such as selection index B of these experiments rather than on the complex selection index such as selection index A. Furthermore, it was realized that the selection index for the selection should be calculated on the basis of the data of some 3-4 agronomic characters-maturity date(X$_1$), branch numbers per plant(X$_2$), stem diameter(X$_3$) and pod numbers per plant etc. It must be noted that it should be successful in selection to select for maturity date(X$_1$) which has high heritability, and the selection index should be calculated easily on the basis of the data of branch numbers per plant(X$_2$), stem diameter(X$_3$) and pod numbers per plant, directly after the harvest before drying and threshing. These characters should be very useful agronomic characters in the selection of Korean soybeans, determinate growing habit type, as they could be measured or counted easily thus saving time and expense in the duration from harvest to drying and threshing, and are affected more in soybean yields than the other agronomic characters.

• Asia pacific journal of information systems
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• v.24 no.4
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• pp.443-472
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• 2014

To survive in the global competitive environment, enterprise should be able to solve various problems and find the optimal solution effectively. The big-data is being perceived as a tool for solving enterprise problems effectively and improve competitiveness with its' various problem solving and advanced predictive capabilities. Due to its remarkable performance, the implementation of big data systems has been increased through many enterprises around the world. Currently the big-data is called the 'crude oil' of the 21st century and is expected to provide competitive superiority. The reason why the big data is in the limelight is because while the conventional IT technology has been falling behind much in its possibility level, the big data has gone beyond the technological possibility and has the advantage of being utilized to create new values such as business optimization and new business creation through analysis of big data. Since the big data has been introduced too hastily without considering the strategic value deduction and achievement obtained through the big data, however, there are difficulties in the strategic value deduction and data utilization that can be gained through big data. According to the survey result of 1,800 IT professionals from 18 countries world wide, the percentage of the corporation where the big data is being utilized well was only 28%, and many of them responded that they are having difficulties in strategic value deduction and operation through big data. The strategic value should be deducted and environment phases like corporate internal and external related regulations and systems should be considered in order to introduce big data, but these factors were not well being reflected. The cause of the failure turned out to be that the big data was introduced by way of the IT trend and surrounding environment, but it was introduced hastily in the situation where the introduction condition was not well arranged. The strategic value which can be obtained through big data should be clearly comprehended and systematic environment analysis is very important about applicability in order to introduce successful big data, but since the corporations are considering only partial achievements and technological phases that can be obtained through big data, the successful introduction is not being made. Previous study shows that most of big data researches are focused on big data concept, cases, and practical suggestions without empirical study. The purpose of this study is provide the theoretically and practically useful implementation framework and strategies of big data systems with conducting comprehensive literature review, finding influencing factors for successful big data systems implementation, and analysing empirical models. To do this, the elements which can affect the introduction intention of big data were deducted by reviewing the information system's successful factors, strategic value perception factors, considering factors for the information system introduction environment and big data related literature in order to comprehend the effect factors when the corporations introduce big data and structured questionnaire was developed. After that, the questionnaire and the statistical analysis were performed with the people in charge of the big data inside the corporations as objects. According to the statistical analysis, it was shown that the strategic value perception factor and the inside-industry environmental factors affected positively the introduction intention of big data. The theoretical, practical and political implications deducted from the study result is as follows. The frist theoretical implication is that this study has proposed theoretically effect factors which affect the introduction intention of big data by reviewing the strategic value perception and environmental factors and big data related precedent studies and proposed the variables and measurement items which were analyzed empirically and verified. This study has meaning in that it has measured the influence of each variable on the introduction intention by verifying the relationship between the independent variables and the dependent variables through structural equation model. Second, this study has defined the independent variable(strategic value perception, environment), dependent variable(introduction intention) and regulatory variable(type of business and corporate size) about big data introduction intention and has arranged theoretical base in studying big data related field empirically afterwards by developing measurement items which has obtained credibility and validity. Third, by verifying the strategic value perception factors and the significance about environmental factors proposed in the conventional precedent studies, this study will be able to give aid to the afterwards empirical study about effect factors on big data introduction. The operational implications are as follows. First, this study has arranged the empirical study base about big data field by investigating the cause and effect relationship about the influence of the strategic value perception factor and environmental factor on the introduction intention and proposing the measurement items which has obtained the justice, credibility and validity etc. Second, this study has proposed the study result that the strategic value perception factor affects positively the big data introduction intention and it has meaning in that the importance of the strategic value perception has been presented. Third, the study has proposed that the corporation which introduces big data should consider the big data introduction through precise analysis about industry's internal environment. Fourth, this study has proposed the point that the size and type of business of the corresponding corporation should be considered in introducing the big data by presenting the difference of the effect factors of big data introduction depending on the size and type of business of the corporation. The political implications are as follows. First, variety of utilization of big data is needed. The strategic value that big data has can be accessed in various ways in the product, service field, productivity field, decision making field etc and can be utilized in all the business fields based on that, but the parts that main domestic corporations are considering are limited to some parts of the products and service fields. Accordingly, in introducing big data, reviewing the phase about utilization in detail and design the big data system in a form which can maximize the utilization rate will be necessary. Second, the study is proposing the burden of the cost of the system introduction, difficulty in utilization in the system and lack of credibility in the supply corporations etc in the big data introduction phase by corporations. Since the world IT corporations are predominating the big data market, the big data introduction of domestic corporations can not but to be dependent on the foreign corporations. When considering that fact, that our country does not have global IT corporations even though it is world powerful IT country, the big data can be thought to be the chance to rear world level corporations. Accordingly, the government shall need to rear star corporations through active political support. Third, the corporations' internal and external professional manpower for the big data introduction and operation lacks. Big data is a system where how valuable data can be deducted utilizing data is more important than the system construction itself. For this, talent who are equipped with academic knowledge and experience in various fields like IT, statistics, strategy and management etc and manpower training should be implemented through systematic education for these talents. This study has arranged theoretical base for empirical studies about big data related fields by comprehending the main variables which affect the big data introduction intention and verifying them and is expected to be able to propose useful guidelines for the corporations and policy developers who are considering big data implementationby analyzing empirically that theoretical base.

• Magazine of the Korean Society of Agricultural Engineers
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• v.16 no.2
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• pp.3361-3394
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• 1974

The purpose of this thesis is to disclose some characteristics of water consumption in relation to the quantities of dry matters through the growing period for two statured varieties of paddy rice which are a tall statured variety and a short one, including the water consumption during seedling period, and to find out the various coefficients of evapotranspiration that are applicable for the water use of an expected yield of the two varieties. PAL-TAL, a tall statured variety, and TONG-lL, a short statured variety were chosen for this investigation. Experiments were performed in two consecutive periods, a seedling period and a paddy field period, In the investigation of seedling period, rectangular galvanized iron evapotranspirometers (91cm${\times}$85cm${\times}$65cm) were set up in a way of two levels (PAL-TAL and TONG-lL varieties) with two replications. A standard fertilization method was applied to all plots. In the experiment of paddy field period, evapotanspiration and evaporation were measured separately. For PAL-TAL variety, the evapotranspiration measurements of 43 plots of rectangular galvanized iron evapotranspirometer (91cm${\times}$85cm${\times}$65cm) and the evaporation measurements of 25 plots of rectangular galvanized iron evaporimeter (91cm${\times}$85cm${\times}$15cm) have been taken for seven years (1966 through 1972), and for TONG-IL variety, the evapotranspiration measurements of 19 plots and the evaporation measurements of 12 plots have been collected for two years (1971 through 1972) with five different fertilization levels. The results obtained from this investigation are summarized as follows: 1. Seedling period 1) The pan evaporation and evapotranspiration during seedling period were proved to have a highly significant correlation to solar radiation, sun shine hours and relative humidity. But they had no significant correlation to average temperature, wind velocity and atmospheric pressure, and were appeared to be negatively correlative to average temperature and wind velocity, and positively correlative to the atmospheric pressure, in a certain period. There was the highest significant correlation between the evapotranspiration and the pan evaporation, beyond all other meteorological factors considered. 2) The evapotranpiration and its coefficient for PAL-TAL variety were 194.5mm and 0.94∼1.21(1.05 in average) respectively, while those for TONG-lL variety were 182.8mm and 0.90∼1.10(0.99 in average) respectively. This indicates that the evapotranspiration for TONG-IL variety was 6.2% less than that for PAL-TAL variety during a seedling period. 3) The evapotranspiration ratio (the ratio of the evapotranspiration to the weight of dry matters) during the seedling period was 599 in average for PAL-TAL variety and 643 for TONG-IL variety. Therefore the ratio for TONG-IL was larger by 44 than that for PAL-TAL variety. 4) The K-values of Blaney and Criddle formula for PAL-TAL variety were 0.78∼1.06 (0.92 in average) and for TONG-lL variety 0.75∼0.97 (0.86 in average). 5) The evapotranspiration coefficient and the K-value of B1aney and Criddle formular for both PAL-TAL and TONG-lL varieties showed a tendency to be increasing, but the evapotranspiration ratio decreasing, with the increase in the weight of dry matters. 2. Paddy field period 1) Correlation between the pan evaporation and the meteorological factors and that between the evapotranspiration and the meteorological factors during paddy field period were almost same as that in case of the seedling period (Ref. to table IV-4 and table IV-5). 2) The plant height, in the same level of the weight of dry matters, for PAL-TAL variety was much larger than that for TONG-IL variety, and also the number of tillers per hill for PAL-TAL variety showed a trend to be larger than that for TONG-IL variety from about 40 days after transplanting. 3) Although there was a tendency that peak of leaf-area-index for TONG-IL variety was a little retarded than that for PAL-TAL variety, it appeared about 60∼80 days after transplanting. The peaks of the evapotranspiration coefficient and the weight of dry matters at each growth stage were overlapped at about the same time and especially in the later stage of growth, the leaf-area-index, the evapotranspiration coefficient and the weight of dry matters for TONG-IL variety showed a tendency to be larger then those for PAL-TAL variety. 4) The evaporation coefficient at each growth stage for TONG-IL and PAL-TALvarieties was decreased and increased with the increase and decrease in the leaf-area-index, and the evaporation coefficient of TONG-IL variety had a little larger value than that of PAL-TAL variety. 5) Meteorological factors (especially pan evaporation) had a considerable influence to the evapotranspiration, the evaporation and the transpiration. Under the same meteorological conditions, the evapotranspiration (ET) showed a increasing logarithmic function of the weight of dry matters (x), while the evaporation (EV) a decreasing logarithmic function of the weight of dry matters; 800kg/10a x 2000kg/10a, ET=al+bl logl0x (bl>0) EV=a2+b2 log10x (a2>0 b2<0) At the base of the weight of total dry matters, the evapotranspiration and the evaporation for TONG-IL variety were larger as much as 0.3∼2.5% and 7.5∼8.3% respectively than those of PAL-TAL variety, while the transpiration for PAL-TAL variety was larger as much as 1.9∼2.4% than that for TONG-IL variety on the contrary. At the base of the weight of rough rices the evapotranspiration and the transpiration for TONG-IL variety were less as much as 3.5% and 8.l∼16.9% respectively than those for PAL-TAL variety and the evaporation for TONG-IL was much larger by 11.6∼14.8% than that for PAL-TAL variety. 6) The evapotranspiration coefficient, the evaporation coefficient and the transpiration coefficient and the transpiration coefficient were affected by the weight of dry matters much more than by the meteorological conditions. The evapotranspiratioa coefficient (ETC) and the evaporation coefficient (EVC) can be related to the weight of dry matters (x) by the following equations: 800kg/10a x 2000kg/10a, ETC=a3+b3 logl0x (b3>0) EVC=a4+b4 log10x (a4>0, b4>0) At the base of the weights of dry matters, 800kg/10a∼2000kg/10a, the evapotranspiration coefficients for TONG-IL variety were 0.968∼1.474 and those for PAL-TAL variety, 0.939∼1.470, the evaporation coefficients for TONG-IL variety were 0.504∼0.331 and those for PAL-TAL variety, 0.469∼0.308, and the transpiration coefficients for TONG-IL variety were 0.464∼1.143 and those for PAL-TAL variety, 0.470∼1.162. 7) The evapotranspiration ratio, the evaporation ratio (the ratio of the evaporation to the weight of dry matters) and the transpiration ratio were highly affected by the meteorological conditions. And under the same meteorological condition, both the evapotranspiration ratio (ETR) and the evaporation ratio (EVR) showed to be a decreasing logarithmic function of the weight of dry matters (x) as follows: 800kg/10a x 2000kg/10a, ETR=a5+b5 logl0x (a5>0, b5<0) EVR=a6+b6 log10x (a6>0 b6<0) In comparison between TONG-IL and PAL-TAL varieties, at the base of the pan evaporation of 343mm and the weight of dry matters of 800∼2000kg/10a, the evapotranspiration ratios for TONG-IL variety were 413∼247, while those for PAL-TAL variety, 404∼250, the evaporation ratios for TONG-IL variety were 197∼38 while those for PAL-TAL variety, 182∼34, and the transpiration ratios for TONG-IL variety were 216∼209 while those for PAL-TAL variety, 222∼216 (Ref. to table IV-23, table IV-25 and table IV-26) 8) The accumulative values of evapotranspiration intensity and transpiration intensity for both PAL-TAL and TONG-IL varieties were almost constant in every climatic year without the affection of the weight of dry matters. Furthermore the evapotranspiration intensity appeared to have more stable at each growth stage. The peaks of the evapotranspiration intensity and transpiration intensity, for both TONG-IL and PAL-TAL varieties, appeared about 60∼70 days after transplanting, and the peak value of the former was 128.8${\pm}$0.7, for TONG-IL variety while that for PAL-TAL variety, 122.8${\pm}$0.3, and the peak value of the latter was 152.2${\pm}$1.0 for TONG-IL variety while that for PAL-TAL variety, 152.7${\pm}$1.9 (Ref.to table IV-27 and table IV-28) 9) The K-value in Blaney & Criddle formula was changed considerably by the meteorological condition (pan evaporation) and related to be a increasing logarithmic function of the weight of dry matters (x) for both PAL-TAL and TONG-L varieties as follows; 800kg/10a x 2000kg/10a, K=a7+b7 logl0x (b7>0) The K-value for TONG-IL variety was a little larger than that for PAL-TAL variety. 10) The peak values of the evapotranspiration coefficient and k-value at each growth stage for both TONG-IL and PAL-TAL varieties showed up about 60∼70 days after transplanting. The peak values of the former at the base of the weights of total dry matters, 800∼2000kg/10a, were 1.14∼1.82 for TONG-IL variety and 1.12∼1.80, for PAL-TAL variety, and at the base of the weights of rough rices, 400∼1000 kg/10a, were 1.11∼1.79 for TONG-IL variety and 1.17∼1.85 for PAL-TAL variety. The peak values of the latter, at the base of the weights of total dry matters, 800∼2000kg/10a, were 0.83∼1.39 for TONG-IL variety and 0.86∼1.36 for PAL-TAL variety and at the base of the weights of rough rices, 400∼1000kg/10a, 0.85∼1.38 for TONG-IL variety and 0.87∼1.40 for PAL-TAL variety (Ref. to table IV-18 and table IV-32) 11) The reasonable and practicable methods that are applicable for calculating the evapotranspiration of paddy rice in our country are to be followed the following priority a) Using the evapotranspiration coefficients based on an expected yield (Ref. to table IV-13 and table IV-18 or Fig. IV-13). b) Making use of the combination method of seasonal evapotranspiration coefficient and evapotranspiration intensity (Ref. to table IV-13 and table IV-27) c) Adopting the combination method of evapotranspiration ratio and evapotranspiration intensity, under the conditions of paddy field having a higher level of expected yield (Ref. to table IV-23 and table IV-27). d) Applying the k-values calculated by Blaney-Criddle formula. only within the limits of the drought year having the pan evaporation of about 450mm during paddy field period as the design year (Ref. to table IV-32 or Fig. IV-22).