• Journal of Korean Society of Forest Science
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• v.52 no.1
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• pp.58-71
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• 1981

Thecodiplosis japonesis is sweeping the Pinus densiflora forests from south-west to north-east direction, destroying almost all the aged large trees as well as even the young ones. The front line of infestation is moving slowly but ceaselessly norhwards as a long bottle front. Estimation is that more than 40 percent of the area of P. densiflora forest has been damaged already, however some individuals could escapes from the damage and contribute to restore the site to the previous vegetation composition. When the stands were attacked by this insect, the drastic openings of the upper story of tree canopy formed by exclusively P. densiflora are usually resulted and some environmental factors such as light, temperature, litter accumulation, soil moisture and offers were naturally modified. With these changes after insect invasion, as the time passes, phytosociologic changes of the vegetation are gradually proceeding. If we select the forest according to four categories concerning the history of the insect outbreak, namely, non-attacked (healthy forest), recently damaged (the outbreak occured about 1-2 years ago), severely damaged (occured 5-6 years ago), damage prolonged (occured 10 years ago) and restored (occured about 20 years ago), any directional changes of vegetation composition could be traced these in line with four progressive stages. To elucidate these changes, three survey districts; (1) "Gongju" where the damage was severe and it was outbroken in 1977, (2) "Buyeo" where damage prolonged and (3) "Gochang" as restored, were set, (See Tab. 1). All these were located in the south temperate forest zone which was delimited mainly due to the temporature factor and generally accepted without any opposition at present. In view of temperature, the amount and distribution of precipitation and various soil factor, the overall homogeneity of environmental conditions between survey districts might be accepted. However this did not mean that small changes of edaphic and topographic conditions and microclimates can induce any alteration of vegetation patterns. Again four survey plots were set in each district and inter plot distance was 3 to 4 km. And again four subplots were set within a survey plot. The size of a subplot was $10m{\times}10m$ for woody vegetation and $5m{\times}5m$ for ground cover vegetation which was less than 2 m high. The nested quadrat method was adopted. In sampling survey plots, the followings were taken into account: (1) Natural growth having more than 80 percent of crown density of upper canopy and more than 5 hectares of area. (2) Was not affected by both natural and artificial disturbances such as fire and thinning operation for the past three decades. (3) Lower than 500 m of altitude (4) Less than 20 degrees of slope, and (5) Northerly sited aspect. An intensive vegetation survey was undertaken during the summer of 1980. The vegetation was devided into 3 categories for sampling; the upper layer (dominated mainly by the pine trees), the middle layer composed by oak species and other broad-leaved trees as well as the pine, and the ground layer or the lower layer (shrubby form of woody plants). In this study our survey was concentrated on woody species only. For the vegetation analysis, calculated were values of intensity, frequency, covers, relative importance, species diversity, dominance and similarity and dissimilasity index when importance values were calculated, different relative weights as score were arbitrarily given to each layer, i.e., 3 points for the upper layer, 2 for the middle layer and 1 for the ground layer. Then the formula becomes as follows; $$R.I.V.=\frac{3(IV\;upper\;L.)+2(IV.\;middle\;L.)+1(IV.\;ground\;L.)}{6}$$ The values of Similarity Index were calculated on the basis of the Relative Importance Value of trees (sum of relative density, frequency and cover). The formula used is; $$S.I.=\frac{2C}{S_1+S_2}{\times}100=\frac{2C}{100+100}{\times}100=C(%)$$ Where: C = The sum of the lower of the two quantitative values for species shared by the two communities. $S_1$ = The sum of all values for the first community. $S_2$ = The sum of all values for the second community. In Tab. 3, the species composition of each plot by layer and by district is presented. Without exception, the species formed the upper layer of stands was Pinus densiflora. As seen from the table, the relative cover (%), density (number of tree per $500m^2$), the range of height and diameter at brest height and cone bearing tendency were given. For the middle layer, Quercus spp. (Q. aliena, serrata, mongolica, accutissina and variabilis) and Pinus densiflora were dominating ones. Genus Rhodedendron and Lespedeza were abundant in ground vegetation, but some oaks were involved also. (1) Gongju district The total of woody species appeared in this district was 26 and relative importance value of Pinus densiflora for the upper layer was 79.1%, but in the middle layer, the R.I.V. for Quercus acctissima, Pinus densiflora, and Quercus aliena, were 22.8%, 18.7% and 10.0%, respectively, and in ground vegetation Q. mongolica 17.0%, Q. serrata 16.8% Corylus heterophylla 11.8%, and Q. dentata 11.3% in order. (2) Buyeo district. The number of species enumerated in this district was 36 and the R.I.V. of Pinus densiflora for the uppper layer was 100%. In the middle layer, the R.I.V. of Q. variabilis and Q. serrata were 8.6% and 8.5% respectively. In the ground vegetative 24 species were counted which had no more than 5% of R.I.V. The mean R.I.V. of P.densiflora ( totaling three layers ) and averaging four plots was 57.7% in contrast to 46.9% for Gongju district. (3) Gochang-district The total number of woody species was 23 and the mean R.I.V. of Pinus densiflora was 66.0% showing greater value than those for two former districts. The next high value was 6.5% for Q. serrata. As the time passes since insect outbreak, the mean R.I.V. of P. densiflora increased as the following order, 46.9%, 57.7% and 66%. This implies that P. densiflora was getting back to its original dominat state again. The pooled importance of Genus Quercus was decreasing with the increase of that for Pinus densiflora. This trend was contradict to the facts which were surveyed at Kyonggi-do area (the central temperate forest zone) reported previously (Yim et al, 1980). Among Genus Quercus, Quercus acutissina, warm-loving species, was more abundant in the southern temperature zone to which the present research is concerned than the central temperate zone. But vice-versa was true with Q. mongolica, a cold-loving one. The species which are not common between the present survey and the previous report are Corpinus cordata, Beltala davurica, Wisturia floribunda, Weigela subsessilis, Gleditsia japonica var. koraiensis, Acer pseudosieboldianum, Euonymus japonica var. macrophylla, Ribes mandshuricum, Pyrus calleryana var. faruiei, Tilia amurensis and Pyrus pyrifolia. In Figure 4 and Table 5, Maximum species diversity (maximum H'), Species diversity (H') and Eveness (J') were presented. The Similarity indices between districts were shown in Tab. 5. Seeing Fig. 6, showing two-dimensional ordination of polts on the basis of X and Y coordinates, Ai plots aggregate at the left site, Bi plots at lower site, and Ci plots at upper-right site. The increasing and decreasing patterns as to Relative Density and Relative Importance Value by genus or species were given in Fig. 7. Some of the patterns presented here are not consistent with the previously reported ones (Yim, et al, 1980). The present authors would like to attribute this fact that two distinct types of the insect attack, one is the short war type occuring in the south temperate forest zone, which means that insect attack went for a few years only, the other one is a long-drawn was type observed at the temperate forest zone in which the insect damage went on continuously for several years. These different behaviours of infestation might have resulted the different ways of vegetational change. Analysing the similarity indices between districts, the very convincing results come out that the value of dissimilarity index between A and B was 30%, 27% between B and C and 35% between A and C (Table 6). The range of similarity index was obtained from the calculation of every possible combinations of plots between two districts. Longer time isolation between communities has brought the higher value of dissimilarity index. The main components of ground vegetation, 10 to 20 years after insect outbreak, become to be consisted of mainly Genus Lespedeza and Rhododendron. Genus Quercus which relate to the top dorminant state for a while after insect attack was giving its place to Pinus densiflora. It was implied that, provided that the soil fertility, soil moisture and soil depth were good enough, Genus Quercuss had never been so easily taken ever by the resistant speeies like Pinus densiflora which forms the edaphic climax at vast areas of forest land. Usually they refer Quercus to the representative component of the undisturbed natural forest in the central part of this country.

• Applied Biological Chemistry
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• v.16 no.2
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• pp.99-111
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• 1973

Percentage recovery or fertilizer nitrogen, phosphorus and potassium by rice plant(Oriza sativa L.) were investigated at 8, 10, 12, 14 kg/10a of N, 6 kg of $P_2O_5$ and 8 kg of $K_2O$ application level in 1967 (51 places) and 1968 (32 places). Two types of nutrient contribution for the yield, that is, P type in which phosphorus firstly increases silicate uptake and secondly silicate increases nitrogen uptake, and K type in which potassium firstly increases P uptake and secondly P increases nitrogen uptake were postulated according to the following results from the correlation analyses (linear) between percentage recovery of fertilizer nutrient and grain or dry matter yields and nutrient uptake. 1. Percentage frequency of minus or zero recovery occurrence was 4% in nitrogen, 48% in phosphorus and 38% in potassium. The frequency distribution of percentage recovery appeared as a normal distribution curve with maximum at 30 to 40 recovery class in nitrogen, but appeared as a show distribution with maximum at below zero class in phosphorus and potassium. 2. Percentage recovery (including only above zero) was 33 in N (above 10kg/10a), 27 in P, 40 in K in 1967 and 40 in N, 20 in P, 46 in Kin 1968. Mean percentage recovery of two years including zero for zero or below zero was 33 in N, 13 in P and 27 in K. 3. Standard deviation of percentage recovery was greater than percentage recovery in P and K and annual variation of CV (coefficient of variation) was greatest in P. 4. The frequency of significant correlation between percentage recovery and grain or dry matter yield was highest in N and lowest in P. Percentage recovery of nitrogen at 10 kg level has significant correlation only with percentage recovery of P in 1967 and only with that of potassium in 1968. 5. The correlation between percentage recovery and dry matter yield of all treatments showed only significant in P in 1967, and only significant in K in 1968, Negative correlation coefficients between percentage recovery and grain or dry matter yield of no or minus fertilizer plots were shown only in K in 1967 and only in P in 1968 indicating that phosphorus fertilizer gave a distinct positive role in 1967 but somewhat' negative role in 1968 while potassium fertilizer worked positively in 1968 but somewhat negatively in 1967. 6. The correlation between percentage recovery of nutrient and grain yield showed similar tendency as with dry matter yield but lower coefficients. Thus the role of nutrients was more precisely expressed through dry matter yield. 7. Percentage recovery of N very frequently had significant correlation with nitrogen uptake of nitrogen applied plot, and significant negative correlation with nitrogen uptake of minus nitrogen plot, and less frequently had significant correlation with P, K and Si uptake of nitrogen applied plot. 8. Percentage recovery of P had significant correlation with Si uptake of all treatments and with N uptake of all treatments except minus phosphorus plot in 1967 indicating that phosphorus application firstly increases Si uptake and secondly silicate increases nitrogen uptake. Percentage recovery of P also frequently had significant correlation with P or K uptake of nitrogen applied plot. 9. Percentage recovery of K had significant correlation with P uptake of all treatments, N uptake of all treatments except minus phosphorus plot, and significant negative correlation with K uptake of minus K plot and with Si uptake of no fertilizer plot or the highest N applied plot in 1968, and negative correlation coefficient with P uptake of no fertilizer or minus nutrient plot in 1967. Percentage recovery of K had higher correlation coefficients with dry matter yield or grain yield than with K uptake. The above facts suggest that K application firstly increases P uptake and secondly phosphorus increases nitrogen uptake for dry matter yied. 10. Percentage recovery of N had significant higher correlation coefficient with grain yield or dry matter yield of minus K plot than with those of minus phosphorus plot, and had higher with those of fertilizer plot than with those of minus K plot. Similar tendency was observed between N uptake and percentage recovery of N among the above treatments. Percentage recovery of K had negative correlation coefficient with grain or-dry matter yield of no fertilizer plot or minus nutrient plot. These facts reveal that phosphorus increases nitrogen uptake and when phosphorus or nitrogen is insufficient potassium competatively inhibits nitrogen uptake. 11. Percentage recovery of N, Pand K had significant negative correlation with relative dry matter yield of minus phosphorus plot (yield of minus plot x 100/yield of complete plot; in 1967 and with relative grain yield of minus K plot in 1968. These results suggest that phosphorus affects tillering or vegetative phase more while potassium affects grain formation or Reproductive phase more, and that clearly show the annual difference of P and K fertilizer effect according to the weather. 12. The correlation between percentage recovery of fertilizer and the relative yield of minus nutrient plat or that of no fertilizer plot to that of minus nutrient plot indicated that nitrogen is the most effective factor for the production even in the minus P or K plot. 13. From the above facts it could be concluded that about 40 to 50 percen of paddy fields do rot require P or K fertilizer and even in the case of need the application amount should be greatly different according to field and weather of the year, especially in phosphorus.