• <tr id="yyy80"></tr>
  • <sup id="yyy80"></sup>
  • <tfoot id="yyy80"><noscript id="yyy80"></noscript></tfoot>
  • 99热精品在线国产_美女午夜性视频免费_国产精品国产高清国产av_av欧美777_自拍偷自拍亚洲精品老妇_亚洲熟女精品中文字幕_www日本黄色视频网_国产精品野战在线观看 ?

    Which trees should be removed in thinning treatments?

    2016-07-05 08:09:20TimoPukkalaErkkihdeandOlaviLaiho
    Forest Ecosystems 2016年1期

    Timo Pukkala,Erkki L?hdeand Olavi Laiho

    ?

    Which trees should be removed in thinning treatments?

    Timo Pukkala1*,Erkki L?hde2and Olavi Laiho2

    Abstract

    Background:In economically optimal management,trees that are removed in a thinning treatment should be selected on the basis of their value,relative value increment and the effect of removal on the growth of remaining trees. Large valuable trees with decreased value increment should be removed,especially when they overtop smaller trees.

    Methods:This study optimized the tree selection rule in the thinning treatments of continuous cover management when the aim is to maximize the profitability of forest management. The weights of three criteria(stem value,relative value increment and effect of removal on the competition of remaining trees)were optimized together with thinning intervals.

    Results and conclusions:The results confirmed the hypothesis that optimal thinning involves removing predominantly large trees. Increasing stumpage value,decreasing relative value increment,and increasing competitive influence increased the likelihood that removal is optimal decision. However,if the spatial distribution of trees is irregular,it is optimal to leave large trees in sparse places and remove somewhat smaller trees from dense places. However,the benefit of optimal thinning,as compared to diameter limit cutting is not usually large in pure one-species stands. On the contrary,removing the smallest trees from the stand may lead to significant (30-40%)reductions in the net present value of harvest incomes.

    Keywords:Continuous cover forestry,Tree selection,High thinning,Optimal management,Spatial distribution,Spatial growth model

    * Correspondence:timo.pukkala@uef.fi

    1University of Eastern Finland,PO Box 111,80101 Joensuu,F(xiàn)inland Full list of author information is available at the end of the article

    ?2015 Pukkala et al. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/),which permits unrestricted use,distribution,and reproduction in any medium,provided you give appropriate credit to the original author(s)and the source,provide a link to the Creative Commons license,and indicate if changes were made.

    Background

    A tree is financially mature for cutting when its relative value increment falls below the guiding rate of interest (Davis and Johnson 1987;Knoke 2012). However,the value increment may improve in the future,due to e.g. changes in the proportions of timber assortments that can be obtained from the tree. Therefore,Duerr et al. (1956)advice to calculate the relative value increment for several coming time periods and classify the tree as financially mature if the highest projected rate of value increase is smaller than the guiding rate of interest.

    In Finland and many other countries the main timber assortments are pulpwood and saw log,of which saw log is more valuable. Figure 1(bottom)shows that,in Finnish conditions,the consequence of unequal prices of different timber assortments is the existence of peaks in relative value increment when the stem attains sufficient dimensions for pulpwood log,first saw log,second saw log etc. At later ages,when most of the volume is already saw log,the peaks gradually disappear and the relative value increment decreases monotonously with increasing tree age and size. If the tree has passed all the value jumps,it is enough to analyze the current value increment to judge whether the tree is financially mature for cutting. It is noteworthy,however,that different trees of the stand do not reach financial maturity at the same age or diameter (Fig. 1 bottom;Knoke 2012). This is because of genetic variation,spatial variation in site productivity,and differences in the competitive positions of the trees. The last factor can be taken into account in financial analysis if distance-dependent growth models are used to predict increment.

    Figure 1(top)shows that a regular spatial distribution of trees makes it possible to maintain sufficient value increments with larger average tree diameter as comparedto more aggregated spatial arrangements. This means that,on the average,a tree reaches financial maturity at smaller diameter in irregular spatial distribution. However,some of the trees in an aggregated spatial tree distribution,growing in sparsely populated places or being surrounded by small trees,may grow better than a similar tree would grow in a regular stand,which postpones the financial maturity of these trees.

    As Davis and Johnson(1987)pointed out,the effect of tree removal on the growth of remaining trees should also be taken into account when deciding when a tree should be harvested and which trees should be removed. Removing a large tree leads to improved value increment in smaller trees. This calls for cutting large trees earlier than their relative value increment suggests. The removal of a large tree may improve the productivity of several remaining trees and increase the relative value increment of the whole residual stand.

    The capital invested in wood production consists of the value of the trees plus the value of bare land. If bare land has a positive value this means that a tree is financially mature at higher relative value increment than indicated by the guiding rate of return. This is because the value increment must be compared to the opportunity cost of the tree and the piece of land occupied by the tree. Bare land value and the effect of tree removal on the growth of remaining trees both increase the rate of value increment at which cutting is optimal decision.

    The above analysis suggests that it is optimal to remove the largest trees in a thinning treatment. However,unequal competitive status of trees,as well as unequal effect of tree removal on the growth of surrounding trees,makes the decision more complicated than for instance applying diameter limit cutting. A detailed analysis of optimal tree selection needs a distance dependent growth model,or at least the calculation of value increments and the effects of tree removal at individual tree level.

    Although the overall principles that should determine tree selection in thinning have been understood and described already several decades ago(Duerr et al. 1956;Davis and Johnson 1987)few studies have actually optimized the selection of removed trees. An exception is the study of Pukkala and Miina(1998)who optimized a tree selection rule which was based on the effect of tree removal on the competitive positions of remaining trees. It was found that it was optimal to thin from above,i.e. remove large trees.

    This study proposed a more straightforward approach to optimizing tree selection:the criteria of the cutting rule were the tree's stumpage value,its value increment,and the effect of removal on the growth of surrounding trees. It was assumed that increasing stumpage value,decreasing relative value increment and increasing competitive effect increase the likelihood of removal. Another hypothesis was that the optimal order of tree removal is less strongly correlated with tree diameter in irregular spatial distributions,as compared to regular tree arrangements. A third hypothesis was that,when net present value is maximized,it is optimal to remove trees at higher relative value increment than the discount rate that is used to calculate net present value.

    Methods

    Three different sample plots of Norway spruce(50 m by 50 m)with the same diameter distribution but different spatial distribution of trees were generated for the analyses. The first plot had a Poisson distribution of trees (henceforth referred to as Poisson stand). The x and y coordinates of trees were drawn from uniform distribution. The second stand was regular,and the third stand was very irregular(aggregated). Regular spatial distributions can be easily achieved by silvicultural treatments and the irregular distribution might be a result of removing all birches from a naturally emerged mixture ofbirch and spruce. Spruces may be very irregularly distributed in such stands. The mean diameter of each initial stand was about 22 cm,stand basal area was 17 m2?ha?1and the number of trees per hectare was 1200. The stands were assumed to grow on mesic site in Central Finland.

    The models of Pukkala et al.(2013)for diameter increment,tree survival and ingrowth were used in simulation. The models are based on about 60,000 diameter increment and survival observations in different stand types. The models can be used in both even-sized and uneven-sized stands. However,the models are not spatial. When the models were used in this study,the predictors which describe competition(stand basal area,G,and basal area in larger trees,BAL)were calculated from trees that were within 10 meters from the tree for which predictions were calculated. This is justified because most of the modelling data of Pukkala et al.(2013)were measured on plots with approximately 10-m radius. However,to make the growth simulator distance dependent,both G and BAL were calculated as the average G (m2?ha?1)or BAL(m2?ha?1)within 10 m,within 10/2 m (5 m),and within 10/3 m(3.33 m). As a result,the closest trees had a larger influence on G and BAL. This is in accordance with several studies,which show that the effect of neighbor trees on the growth of a subject tree decreases with increasing distance(see e.g. Miina and Pukkala 2000).

    Mortality was simulated by comparing the tree's survival probability to random number distributed uniformly between 0 and 1. If the random number was larger than the survival probability,the tree was assigned as dead. Ingrowth(number of ingrowth trees)was predicted with the unmodified model of Pukkala et al.(2013)but spatial criterion was used to choose the places for ingrowth trees. Fifty candidate positions were generated for each ingrowth tree and the competition index proposed by Miina and Pukkala(2000;their Equation 6b)was calculated for each location. The tree was placed to that location which had the lowest completion index. The procedure mimics the observed dynamics of spruce stands(e.g. Eerik?inen et al. 2007)in which ingrowth trees appear in openings and places with little competition by larger trees.

    The optimization problem consisted of selecting the thinning intervals and the weights of the three criteria of the following tree selection rule:

    where Value is the stumpage value of the stem,Relative-ValueIncrement is the predicted 5-year value increment divided by the stumpage value of the stem,BALeffect is the total reduction in the BALs of neighbor trees in case the tree is removed,and w1,w2and w3are optimized parameters which determine the effect of the three criteria on the order of tree removal. The BAL effect was calculated in the same way as BALs were calculated in growth prediction,i.e. by giving more weight to close neighborhood. A removed tree affected most within 3.33 m,somewhat less within 3.33-5 m,still less within 5-10 m and not at all beyond 10 meters.

    The optimized parameters were thinning intervals and parameters w1,w2and w3of the tree selection rule. The problem formulations correspond to continuous cover management since planting was not an option when stand development was simulated. The remaining basal area was calculated with the following model,which is based on 20,583 optimized cuttings of 6,861 stands located in different parts of Finland. All optimizations meet the constraints of Finnish forestry legislation:

    where Gremainis remaining basal area(m2?ha?1),D is basal-area-weighted mean diameter of trees(cm),Gtotalis stand basal area before thinning(m2?ha?1),Gpineis basal area of pine(m2?ha?1),Gspruceis basal area of spruce (m2?ha?1),r is discount rate(%),and MT,VT and CT are indicator variables for mesic,sub-xeric and xeric site,respectively.

    When a thinning was simulated,trees were removed according to their removal score(Eq. 1)until the remaining basal area was equal to the value calculated with Eq. 2. In another set of optimizations,also the remaining basal was optimized. These optimizations may not always meet the current legal limits of Finland.

    A 10-m wide buffer zone was generated around the plot when stand development was simulated(when computing the predictors of the models),and the buffer was removed after completing a simulation time step. The buffer was generated by assuming that the plot was surrounded by similar plots on all sides. Since the models that were used in growth simulation have five-year time step,stand development was simulated in 5-year steps.

    Computation of the removal scores of trees involved the calculation of the stumpage value,5-year value increment and BAL effect for every tree. To obtain the stumpage value,taper models(Laasasenaho 1982)were used to calculate assortment volumes,which were multiplied by their unit prices. The assortments were saw log(50€?m?3,minimum top diameter 16 cm,minimum length 4 m),and pulpwood(15€?m?3,minimum top diameter 9 cm,minimum length 2 m). To calculate value increment,the diameter(dbh)and height of the tree were incremented by five-year growth,and assortment volumes corresponding to the incremented dimensions were calculated with the taper model.

    The obtained stumpage values,value increments and BAL effects were used to calculate the removal scores for all trees,and the tree with the highest score was removed. Since a tree removal may affect the competitive influences,value increments and removal scores of remaining trees,the BAL effects and value increment predictions of all remaining trees were updated after every tree removal. This involved removing the buffer,generating the buffer again,and calculating the BAL effects and value increments again. Removing and adding the buffer after every tree removal was based on the assumption that the forest that surrounds the plot is thinned simultaneously with the plot.

    Three next thinnings were optimized in the analyses of this study. The NPV of the ending growing stock(residual stand after the third thinning)was predicted with the model(see Pukkala 2015b). The model prediction,once discounted to the starting year of simulation,gives the NPV of all incomes and costs that are later than the last optimized cutting. It has been shown(Pukkala 2015a,2015b)that using the model for estimate the NPV of distant cuttings has no major effect on the optimization results for the next cuttings,as compared to a higher number of optimized cuttings.

    Since the simulation involves stochasticity in mortality and ingrowth,every simulation that was conducted during the optimization run was repeated 10 times and the mean NPV of the repeated simulations was used as the objective function(returned to the optimization algorithm). The direct search method of Hooke and Jeeves (1961)was used in optimization. Every optimization was repeated 5 times,each direct search starting from the best of 100 random combinations of optimized variables. The best solution(highest NPV)was taken as the optimal solution. NPV was calculated with 3%discount rate.

    Table 1 Net present values and optimal values of decision variables for Poisson,regular and irregular stand in 5 repeated optimizations when the remaining basal area of thinning was calculated with a model(Eq. 2)

    Results and discussion

    Remaining basal area not optimized

    Optimizations in which the remaining basal area was not optimized suggested immediate thinning in all three stands,another thinning after 10 years,and a third thinning 10 years later(Table 1,boldface). However,there was some variation between repeated optimizations in the cutting intervals,especially in the irregular stand. Looking at the NPVs of the solutions suggests that those solutions that propose intervals other than 10 years may be sub-optimal,i.e.,the algorithm has converged to local optimum.

    The ranking of the three stands in terms of NPV was logical. The regular stand produced the highest NPV whereas the highly irregular stand produced clearly smaller economic benefit than the other stands(Table 1).The signs of the parameters of the removal score function were logical:high stem value and high BAL effect (high reduction in the competition of remaining trees)increased the score,and high relative value increment decreased it. Valuable trees with low value increment and strong competitive effect on other trees were the first ones to leave. As a result,thinnings from above were conducted,as can be seen from the maps of Fig. 2.

    A closer inspection of the diameters of the removed and remaining trees(Fig. 3)revealed that the thinnings of the regular stand resembled diameter limit cutting. There were only one or two diameter classes which included both remaining and removed trees. In the first thinning of the Poisson forest,there were 4 diameter classes(8 cm diameter range)having both remaining and removed trees. This range was 12 cm in the irregular stand. Large trees were left in sparse places and rather small trees were removed from dense places.

    The diagrams of Fig. 4(bottom right)show that,in the regular stand,the order of removal followed decreasing breast height diameter fairly closely. The selection score correlated closely with both dbh and relative value increment,suggesting that either of these variables alone could be used as the harvesting criterion in a regular stand. In the other stands,the order of removal did not follow decreasing dbh equally closely. In both Poisson and regular stands,most trees removed in the first thinning were larger than 25 cm.

    The situation was different on the irregular stand,in which several removed trees were smaller than 25 cm. To have a sufficient remaining basal area,some large trees were left to continue growing(Figs. 2 and 3). In the irregular stand,the removal score did not correlate strongly with dbh or relative value increment. In this stand,the BAL effect of tree removal varied much more than in the other stands(Fig. 4 bottom left)and had a stronger influence on the removal score than in the other stands. This cannot be concluded from the weights of BAL effect in Table 1 since the effect of the criterion depends on both the weight and the range of variation in the criterion variable in a particular stand and thinning. As a conclusion,when thinning an irregular stand,more importance should be given to the reduction of competition due to tree removal.

    Table 1shows that repeated optimizations result in different weights of the criteria of the removal score,suggesting that there is much uncertainty in this respect. However,since the three criteria correlate with each other,the removal score may be fairly similar with different weights. This means that,although there is uncertainty about the exact weights of the removal criteria,there is less uncertainty about the removal order of trees. This can be seen from Fig. 5,which shows the locations of trees removed in the first thinning of the Poisson forest when the five different solutions of Table 1 were used to select the trees. Almost the same trees were removed when using different values for weights w1,w2and w3. Three trees were removed in only 1 solution and another three trees were removed in less than five solutions. All the other trees were removed in all solutions. Therefore,high variation in the obtained weights of the removal criteria bring only little uncertainty in tree selection.

    Another simulation was done by applying the optimal weights of the thinning rules(boldface rows in Table 1)of the three different stands to the first thinning of the Poisson stand(Fig. 6). The results show again that nearly the same trees were removed from the Poisson forest when applying the optimal tree selection rule of regular,irregular or Poisson stand. This result implies that the same tree selection rule could be used in all spatial distributions,which may sound counterintuitive in the light of the diagrams of Fig. 4. However,even though theweight of e.g. BAL effect would be the same in all stands,the influence of BAL effect on tree removal would be stronger in the irregular stand,due to greater variation of BAL effect in this stand.

    Remaining basal area optimized

    When the remaining basal area was optimized together with the thinning intervals and tree selection rule,the NPV of the optimal schedule increased by 4-5%as compared to optimizations in which remaining basal area was calculated with Eq. 2(Tables 1 and 2). The remaining basal areas were now 6.5-8.9 m2?ha?1whereas they were 10-11 m2?ha?1when calculated with the model. The thinning intervals became longer in the Poisson and regular stands. It can be concluded that the requirement for a certain minimum residual basal area decreased the profitability of forest management in the three analyzed stands.

    As a consequence of stronger thinnings,more ingrowth appeared in the stands,especially in the Poisson and regular stands,as compared to previous optimizations(Fig. 7). The thinning was extended to smaller diameters(Fig. 8),and the diameter range that had both remaining and removed trees,became narrower. This means that when remaining basal area was not restricted,optimal thinning resembled more diameter limit cutting,also in the aggregated spatial distribution.

    The optimality of diameter limit cutting was further inspected by removing the trees according to dbh,starting from the largest tree(‘High thinning' in Fig. 9). For comparison,simulations were also conducted so that the smallest trees were removed in the thinning treatments(‘Low thinning' in Fig. 9). The results show that systematic diameter limit cutting was not much worse than optimal tree selection. Thinning from below would be a clearly inferior management approach.

    Effect of discount rate

    The previous optimizations used 3%discount rate. When remaining basal area was optimized in the Poisson stand (together with thinning intervals and tree selection rule),lower discount rate(1%)led to clearly longer thinning intervals,higher pre-thinning basal areas,larger mean tree diameters,and slightly higher post-thinning basal areas and mean diameters,compared to 3%discount rate(Fig. 10). Increased discount rate(5%)resulted in 10-year thinning interval,lower remaining basal area and smaller mean tree size of the residual stand. When the discount rate was 1%,the largest trees of the stand were 35 cm in dbh when the thinning was conducted. With 5%discount rate the thinning was conducted when the largest trees reached 25 cm breast height diameter.

    The effect of discount rate was the most clear in growing stock value(Fig. 10,bottom left). When the discount rate was 1%,the stumpage value of the growing stock was 8000€?ha?1at the second thinning and 13 000€?ha?1at the third thinning. With 5%discount rate the prethinning growing stock value was only about 2200€?ha?1.

    When NPV was maximized with 1%discount rate,it was optimal to thin the stand when its relative value increment was 2-3%. With 3%discount rate the stand was thinned at 4.5-6%value increment,and at with 5% discount rate at 8%value increment. When the discount rate was 3%,in the first thinning of the Poisson stand about half if the removed trees had a relative value increment of about 2%and the rest had 3-6%rate of value increment(Fig. 11,top). However,the first thinning,which was immediately,was most probably later than its optimal timing would have been. In later thinnings,50%of removed trees were removed at about 4% rate of value increment and the rest were removed at higher,up to 7%value increment.

    In the second thinning of the Poisson stand,trees were removed at 2-7%value increments when discount rate was 1%,at 3-7%increments when discount rate was 3%and at 5-14%value increments when discount rate was 5%(Fig. 11,bottom). This is in line with the hypothesis of the study,according to which it is optimal to remove a tree at higher relative value increment than the guiding rate of interest. This is because of the opportunity cost of bare land and the fact that tree removal improves the relative value increment of remaining trees.

    Table 2 Net present values and optimal values of decision variables for Poisson,regular and irregular stand when the remaining basal area(G)was optimized together with thinning intervals and tree selection rule

    Conclusions

    The results suggest that it is nearly optimal to select the trees that are removed in a thinning treatment on the basis of breast height diameter,starting from the largest tree. However,in irregular spatial distributions,the competition faced by the tree and the effect of removal onthe growth of surrounding trees should also be taken into account. The degree of irregularity of the irregular stand of this study was so high that such stands are rarely encountered in managed forests. Therefore,diameter limit cutting seems to be a sufficient approach in most stands. However,there are other types of irregularity,which are more common. For example,the stand may have sub-areas of predominantly large trees while other sub-areas are occupied by smaller trees. In this case,diameter limit cutting leads to openings and unutilized growing space. Since decreased competition increases the dbh of financial maturity,it would most probably be better to leave some large trees to continue growing. This means that a tree selection rule that incorporates several criteria is more likely to work better (than dbh alone)in a wide range of stand structures. In mixed stands,relative value increment or a more complicated tree selection rule is certainly better than using only dbh to select the removed trees(Knoke 2012). This is because of differences in the inherent growth rates,assortments dimensions,and assortment prices of different species.

    Although there are very few previous studies on optimal tree selection,several recent results on pre-and post-cutting diameter distributions in economically optimal uneven-aged management support the conclusion that optimal cutting resembles diameter-limit cutting (Tahvonen et al. 2010;Tahvonen 2011;Pukkala et al. 2014;Pukkala 2015a,2015b). The American studies conducted during the 1980s lead to similar conclusions (Haight 1985,1987;Haight and Getz 1987). Also longterm silvicultural trials support the conclusion than diameter limit cutting is more profitable than single-tree selection. For example,in the Vessari experiment located in Central Finland,the net present value(calculated with 3%discount rate)of diameter limit cutting was 13750€?ha?1whereas it was only 10250€?ha?1in single tree selection during a 40-year monitoring period. In the nearby Honkam?ki experiment the NPV of diameter limit cutting was 10500€?ha?1and the NPV of single tree selection was 7800€/ha(Pukkala et al. 2012).

    The optimizations of this study were done for continuous cover management. However,the same principles of analyzing the financial maturity of trees also apply toeven-aged management. Several studies have shown that the optimal thinning of a certain stand would be rather similar in even-aged management and continuous cover forestry(e.g. Pukkala 2015b). In the study of Pukkala et al. (2014),which optimized the cuttings of 200 different stands representing different stand structures without the limitation to pursue either even-aged management or continuous cover forestry,97-99%of thinnings were high thinnings similar to those that were found optimal in this study. However,some studies(Valsta 1992;Hyyti?inen et al. 2005;Pukkala 2015b)have found that,in evenaged management when a forced clear-felling belongs to the management schedule,it is sometimes optimal to remove trees from both ends of the diameter distribution.

    Tree quality,health and vigor are additional characteristics which should affect tree selection. However,these criteria are difficult to include in simulation and optimization studies. As suggested already M?ller (1922),trees whose vigor is decreased should be removedin thinnings. If low-quality trees overtop smaller and better-quality individuals,they should also be removed,as commonly done in forestry practice. If the quality of all trees is low(or equal),the criteria proposed in this study can be used.

    The optimal tree selection rules that were developed in this study,all lead to thinning from above. The comparison of Fig. 9 also shows that thinning from below may not be economically justified. This can also be concluded from earlier literature that discusses financial maturity(Duerr et al. 1956;Davis and Johnson 1987;Knoke 2012). Also several optimization studies show that high thinning is in most cases more profitable than low thinning(e.g.,Haight and Monserud 1990;Valsta 1992;Tahvonen et al. 2013). In fact,it is hard to find economic arguments which would justify the use of low thinning.

    The study used a non-spatial model in spatial simulation. It was justified by the fact that the area of computing the competition variables(G and BAL)corresponds to the area of the sample plots in the modelling data of Pukkala et al.(2013). However,there is one difference:in the modelling data,G and BAL were computed within 300-m2plots(around 10 m radius)for all trees of the plot,not only for trees near plot center. These values of G and BAL may not describe the competition that edge trees face in the best possible way because trees outside the plot also create competition. This may be called as“sampling error”in the calculation of G and BAL for the edge trees in individual-tree growth modelling. Sampling error results in weaker relationship(underestimated influence)between growth and G or BAL in the growth model. In the current study,this underestimation was counteracted by the distance-dependent computation of G and BAL,which increased their variation. As a result,the predictions may in fact be better than when calculating G and BAL in the same way as they were computed in the data preparation step of growth modelling.

    The simulations of this study included stochasticity in mortality and ingrowth. Therefore,every simulationwas repeated 10 times and the mean NPV of the 10 simulations was used as the value of the management schedule. Ten simulations is a small number in stochastic simulation. However,few trees die in managed forests,and dead trees are usually small. Ingrowth begins to affect harvest removals and stand value gradually,its effect being minimal during the first decades although ingrowth is critically important for the long-term sustainability of continuous cover forestry. Therefore,it can be concluded that the two sources of stochasticity(mortality and ingrowth)did not have any significant effect on the results of this study.

    Competing interests

    The authors declare that they have no competing interests.

    Authors’contributions

    TP conducted the analyses. EL and OL participated in the writing of the manuscript. All authors have read and approved the final manuscript.

    Author details

    1University of Eastern Finland,PO Box 111,80101 Joensuu,F(xiàn)inland.2Joen Forest Program Consulting,Rauhankatu 41,80100 Joensuu,F(xiàn)inland.

    Received:23 September 2015 Accepted:13 December 2015

    References

    Davis LS,Johnson KN(1987)Forest management. Third edition. McGraw-Hill Inc. p 790

    Duerr WA,F(xiàn)edkiw J,Guttenberg S(1956)Financial maturity:A guide to profitable timber growing. US Dep Agric Tech Bull 1146:74

    Eerik?inen K,Miina J,Valkonen S(2007)Models for the regeneration establishment and the development of established seedlings in uneven-aged,Norway spruce dominated stands of southern Finland. Forest Ecol Manage 242:444-461

    Haight RG(1985)A comparison of dynamic and static economic models of uneven-aged stand management. Forest Sci 31(4):957-974

    Haight RG(1987)Evaluating the efficiency of even-aged uneven-aged stand management. Forest Sci 33(1):116-134

    Haight RG,Getz WM(1987)Fixed and equilibrium problems in uneven-aged stand management. Forest Sci 33(4):908-931

    Haight RG,Monserud RA(1990)Optimizing any-aged management of mixed-species stands:II. Effects of decision criteria. Forest Sci 36(1):125-144

    Hooke R,Jeeves TA(1961)“Direct search”solution of numerical and statistical problems. J ACM 8:212-229

    Hyyti?inen K,Tahvonen O,Valsta L(2005)Optimum juvenile density,harvesting and stand structure in even-aged Scots pine stands. Forest Sci 51:120-133

    Knoke T(2012)The economics of continuous cover forestry. In:Pukkala T,Gadow KV(eds)Continuous Cover Forestry. Springer. ISBN 978-94-007-2201-9. pp 167-193

    Laasasenaho J(1982)Taper curve and volume equations for pine spruce and birch. Communicationes Instuti Forestalis Fenniae 108:1-74

    Miina J,Pukkala T(2000)Using numerical optimization for specifying individual-tree competition models. Forest Sci 46(2):277-281

    M?ller A(1922)Der Dauerwaldgedanke:sein Sinn und seine Bedeutung. Springer,Berlin,p 84

    Pukkala T(2015a)Optimizing continuous cover management of boreal forest when timber prices and tree growth are stochastic. Forest Ecosyst 2(6):1-13

    Pukkala T(2015b)Plenterwald,Dauerwald,or clearcut?Forest Policy Econ 2016(62):125-134,http://dx.doi.org/10.1016/j.forpol.2015.09.002

    Pukkala T,Miina J(1998)Tree-selection algorithms for optimizing thinning using a distance-dependent growth model. Can J Forest Res 28:693-702

    Pukkala T,L?hde E,Laiho O(2012)Continuous cover forestry in Finland - Recent research results. In:Pukkala T,Von Gadow K(eds)Continuous Cover Forestry. Springer.,pp 85-128

    Pukkala T,L?hde E,Laiho O(2013)Species interactions in the dynamics of even- and uneven-aged boreal forests. J Sustain Forest 32:1-33

    Pukkala T,L?hde E,Laiho O(2014)Optimizing any-aged management of mixed boreal forest under residual basal area constraints. J Forest Res 23(3):727-636

    Tahvonen O(2011)Optimal structure and development of uneven-aged Norway spruce forests. Canadian Journal of Forest Res 42:2389-2402

    Tahvonen O,Pukkala T,Laiho O,L?hde E,Niinim?ki S(2010)Optimal management of uneven-aged Norway spruce stands. Forest Ecol Manage 260:106-115

    Tahvonen O,Pihlainen S,Niinim?ki S(2013)On the economics of optimal timber production in boreal Scots pine stands. Can J Forest Res 43:719-730

    Valsta L(1992)A scenario approach to stochastic anticipatory optimization in stand management. Forest Sci 38(2):430-447

    日韩大片免费观看网站| 伦理电影免费视频| 大香蕉97超碰在线| 久久久久久人妻| 欧美变态另类bdsm刘玥| 欧美日韩一区二区视频在线观看视频在线| 中文欧美无线码| 黑人猛操日本美女一级片| 日韩免费高清中文字幕av| 天堂俺去俺来也www色官网| 免费少妇av软件| 成人亚洲精品一区在线观看| 亚洲国产av影院在线观看| 国产成人av激情在线播放 | 欧美 日韩 精品 国产| 精品久久久噜噜| 亚洲精品美女久久av网站| 伊人亚洲综合成人网| 搡女人真爽免费视频火全软件| 欧美性感艳星| 国产亚洲一区二区精品| 国产伦理片在线播放av一区| 亚洲精品久久久久久婷婷小说| 又粗又硬又长又爽又黄的视频| 午夜91福利影院| 18禁动态无遮挡网站| 亚洲精品一二三| 久久久久久久久久久久大奶| 亚洲精品456在线播放app| 欧美日韩av久久| 亚洲美女视频黄频| 成人国产麻豆网| 久久久久视频综合| 成人漫画全彩无遮挡| 高清不卡的av网站| 午夜老司机福利剧场| 一二三四中文在线观看免费高清| 精品国产国语对白av| 免费观看的影片在线观看| 亚洲精品日本国产第一区| 国产无遮挡羞羞视频在线观看| 蜜桃久久精品国产亚洲av| 人人澡人人妻人| 亚洲,欧美,日韩| 中文字幕久久专区| 伊人久久国产一区二区| 日韩 亚洲 欧美在线| 九色成人免费人妻av| 天堂中文最新版在线下载| 亚洲精品一二三| 中文字幕免费在线视频6| 美女国产高潮福利片在线看| 一区二区三区四区激情视频| 精品久久久久久久久av| 观看av在线不卡| 精品熟女少妇av免费看| 中国美白少妇内射xxxbb| 丁香六月天网| 麻豆精品久久久久久蜜桃| 18禁观看日本| 亚洲av成人精品一区久久| 国产精品国产av在线观看| 99国产综合亚洲精品| 一级,二级,三级黄色视频| 3wmmmm亚洲av在线观看| 一本—道久久a久久精品蜜桃钙片| av天堂久久9| 日韩欧美一区视频在线观看| 免费看不卡的av| 波野结衣二区三区在线| 欧美精品一区二区免费开放| 极品人妻少妇av视频| 日日撸夜夜添| 久久午夜综合久久蜜桃| 国产欧美日韩一区二区三区在线 | 日韩强制内射视频| 日韩一区二区三区影片| 免费看光身美女| 哪个播放器可以免费观看大片| 天堂8中文在线网| 热re99久久精品国产66热6| 午夜福利,免费看| 国产精品国产三级专区第一集| 亚洲国产精品一区三区| 国产视频内射| 成年人免费黄色播放视频| 久久久久精品久久久久真实原创| 嫩草影院入口| 老女人水多毛片| 肉色欧美久久久久久久蜜桃| 一级片'在线观看视频| 美女国产高潮福利片在线看| 国产伦理片在线播放av一区| 少妇丰满av| 99久久综合免费| 精品亚洲成国产av| 夜夜看夜夜爽夜夜摸| 18禁裸乳无遮挡动漫免费视频| 国产精品熟女久久久久浪| 国产色婷婷99| 亚洲av国产av综合av卡| av在线播放精品| 成年av动漫网址| 免费高清在线观看日韩| 爱豆传媒免费全集在线观看| 水蜜桃什么品种好| 久久久久国产网址| 亚洲av.av天堂| 婷婷色综合www| 久久国产精品男人的天堂亚洲 | 亚洲av福利一区| 九色亚洲精品在线播放| 成年人免费黄色播放视频| 亚洲av成人精品一二三区| 精品国产国语对白av| tube8黄色片| 人妻夜夜爽99麻豆av| 精品久久国产蜜桃| 飞空精品影院首页| 欧美日韩综合久久久久久| 一本—道久久a久久精品蜜桃钙片| 日韩亚洲欧美综合| 一区二区日韩欧美中文字幕 | 亚洲欧洲国产日韩| 久久久久精品性色| 久久鲁丝午夜福利片| 极品人妻少妇av视频| 亚洲四区av| av视频免费观看在线观看| 在线观看免费日韩欧美大片 | 亚洲第一区二区三区不卡| 2018国产大陆天天弄谢| 嘟嘟电影网在线观看| 女性生殖器流出的白浆| 一本—道久久a久久精品蜜桃钙片| 精品一区二区三区视频在线| 久久精品国产亚洲av涩爱| 欧美bdsm另类| 成年人午夜在线观看视频| 少妇的逼水好多| av播播在线观看一区| 少妇猛男粗大的猛烈进出视频| 97在线人人人人妻| a级毛片在线看网站| 国产综合精华液| 狠狠精品人妻久久久久久综合| 国产高清不卡午夜福利| a级毛片免费高清观看在线播放| 欧美丝袜亚洲另类| 看十八女毛片水多多多| 一级毛片电影观看| 高清在线视频一区二区三区| 午夜日本视频在线| 午夜91福利影院| 久久精品熟女亚洲av麻豆精品| 亚洲精品日韩在线中文字幕| 精品卡一卡二卡四卡免费| 欧美97在线视频| freevideosex欧美| 婷婷色综合www| 80岁老熟妇乱子伦牲交| 街头女战士在线观看网站| 久久 成人 亚洲| 久久久久久久亚洲中文字幕| 观看av在线不卡| 国产在视频线精品| 99re6热这里在线精品视频| 日韩不卡一区二区三区视频在线| 亚洲欧美一区二区三区国产| 免费av不卡在线播放| 在线观看三级黄色| 成年av动漫网址| 成人毛片60女人毛片免费| 人人澡人人妻人| 97在线人人人人妻| 国产一区二区三区av在线| 亚洲欧美日韩卡通动漫| 秋霞伦理黄片| 国产伦精品一区二区三区视频9| 热99国产精品久久久久久7| 久久精品国产a三级三级三级| 一本一本久久a久久精品综合妖精 国产伦在线观看视频一区 | 最黄视频免费看| 欧美性感艳星| 亚洲精品成人av观看孕妇| 久久精品国产鲁丝片午夜精品| 免费看光身美女| 日本与韩国留学比较| 欧美另类一区| 亚洲国产色片| 成年女人在线观看亚洲视频| 交换朋友夫妻互换小说| av免费观看日本| 国产视频首页在线观看| 人妻少妇偷人精品九色| 午夜福利视频在线观看免费| 免费av不卡在线播放| 亚洲欧洲国产日韩| 91精品伊人久久大香线蕉| 久久久久久久大尺度免费视频| 午夜福利,免费看| 欧美三级亚洲精品| 桃花免费在线播放| 精品少妇内射三级| 精品99又大又爽又粗少妇毛片| 女性生殖器流出的白浆| 视频区图区小说| 在线观看免费高清a一片| 超碰97精品在线观看| 久久av网站| freevideosex欧美| 亚洲av电影在线观看一区二区三区| 亚洲人与动物交配视频| 久久毛片免费看一区二区三区| 日韩av不卡免费在线播放| 国产亚洲最大av| 久久久久网色| 欧美一级a爱片免费观看看| 欧美xxⅹ黑人| 在线观看三级黄色| 热99国产精品久久久久久7| 美女中出高潮动态图| 中文字幕人妻丝袜制服| 我的女老师完整版在线观看| 插阴视频在线观看视频| 99久久人妻综合| 久久精品人人爽人人爽视色| 亚洲欧洲国产日韩| 王馨瑶露胸无遮挡在线观看| 自线自在国产av| 人人妻人人澡人人爽人人夜夜| 一本—道久久a久久精品蜜桃钙片| av不卡在线播放| 又黄又爽又刺激的免费视频.| 国产精品国产三级专区第一集| 丰满少妇做爰视频| 国产成人freesex在线| 精品久久蜜臀av无| 国产在线一区二区三区精| 日产精品乱码卡一卡2卡三| 精品久久久精品久久久| 亚洲精品美女久久av网站| 国产国拍精品亚洲av在线观看| 飞空精品影院首页| 欧美日韩成人在线一区二区| 久久久久精品性色| 肉色欧美久久久久久久蜜桃| xxxhd国产人妻xxx| 考比视频在线观看| 777米奇影视久久| 飞空精品影院首页| 只有这里有精品99| 水蜜桃什么品种好| 久久99一区二区三区| 免费观看无遮挡的男女| 97超碰精品成人国产| 我的女老师完整版在线观看| 黑人巨大精品欧美一区二区蜜桃 | 亚洲综合色惰| 香蕉精品网在线| 永久免费av网站大全| 91久久精品国产一区二区成人| 精品人妻在线不人妻| 久久久久久伊人网av| 超碰97精品在线观看| 纵有疾风起免费观看全集完整版| 午夜老司机福利剧场| 伊人久久精品亚洲午夜| 成人18禁高潮啪啪吃奶动态图 | 久久久久精品久久久久真实原创| 国产精品久久久久久精品古装| 欧美精品一区二区大全| 美女cb高潮喷水在线观看| 精品少妇黑人巨大在线播放| 丰满乱子伦码专区| 精品视频人人做人人爽| 日韩三级伦理在线观看| 国产日韩欧美亚洲二区| 一区在线观看完整版| 狂野欧美白嫩少妇大欣赏| 九九在线视频观看精品| 国产精品久久久久久av不卡| 免费看光身美女| 精品一区在线观看国产| 精品一品国产午夜福利视频| 亚洲av福利一区| 久久精品国产鲁丝片午夜精品| 如日韩欧美国产精品一区二区三区 | 黄色怎么调成土黄色| 精品亚洲成a人片在线观看| 如日韩欧美国产精品一区二区三区 | 一级爰片在线观看| 久久久久久久久久久久大奶| 一级片'在线观看视频| 久久久久网色| 热99久久久久精品小说推荐| 午夜日本视频在线| 最近中文字幕高清免费大全6| 九九爱精品视频在线观看| av.在线天堂| 亚洲欧洲日产国产| 少妇熟女欧美另类| 伦理电影大哥的女人| 色吧在线观看| 最近2019中文字幕mv第一页| 人人妻人人爽人人添夜夜欢视频| 少妇人妻久久综合中文| av女优亚洲男人天堂| 国产伦理片在线播放av一区| 精品国产一区二区久久| 美女cb高潮喷水在线观看| 80岁老熟妇乱子伦牲交| 日本av免费视频播放| 亚洲综合精品二区| 免费看不卡的av| 亚洲国产av影院在线观看| 97精品久久久久久久久久精品| 精品亚洲乱码少妇综合久久| 成人无遮挡网站| 欧美97在线视频| 精品国产乱码久久久久久小说| 国产精品久久久久成人av| 国产精品国产av在线观看| 精品一区二区免费观看| 亚洲欧美日韩另类电影网站| 黄色一级大片看看| 高清av免费在线| 国产精品一区www在线观看| 欧美日本中文国产一区发布| 精品一区二区免费观看| 熟女人妻精品中文字幕| 亚洲久久久国产精品| 亚洲伊人久久精品综合| av不卡在线播放| 亚洲精华国产精华液的使用体验| 久久久亚洲精品成人影院| 久久午夜综合久久蜜桃| 成人二区视频| 大陆偷拍与自拍| 精品久久久久久久久av| 国产精品久久久久久精品古装| 黑丝袜美女国产一区| 一级a做视频免费观看| 国产无遮挡羞羞视频在线观看| 亚洲成人av在线免费| 夫妻午夜视频| 狠狠精品人妻久久久久久综合| 大话2 男鬼变身卡| 一级二级三级毛片免费看| 亚洲少妇的诱惑av| 婷婷色麻豆天堂久久| 91精品伊人久久大香线蕉| 国产日韩欧美在线精品| 午夜av观看不卡| 99久久精品国产国产毛片| av在线老鸭窝| 欧美精品一区二区免费开放| 一区二区三区免费毛片| 欧美激情 高清一区二区三区| 最近最新中文字幕免费大全7| 久久精品久久久久久久性| 99国产综合亚洲精品| 2018国产大陆天天弄谢| 国产精品久久久久久久久免| 亚洲第一区二区三区不卡| 黄色怎么调成土黄色| 久久久久久久亚洲中文字幕| 成人手机av| 婷婷色综合www| 91精品三级在线观看| 亚洲av福利一区| 亚洲av不卡在线观看| 美女大奶头黄色视频| 亚洲高清免费不卡视频| 国产av一区二区精品久久| 人人妻人人澡人人看| 51国产日韩欧美| 亚洲经典国产精华液单| 精品一区在线观看国产| 极品少妇高潮喷水抽搐| 如日韩欧美国产精品一区二区三区 | 少妇猛男粗大的猛烈进出视频| 国产一区二区三区综合在线观看 | 飞空精品影院首页| 日韩精品有码人妻一区| 涩涩av久久男人的天堂| 国产男人的电影天堂91| 在线观看免费视频网站a站| 国产精品三级大全| 中国美白少妇内射xxxbb| 黑人欧美特级aaaaaa片| www.av在线官网国产| 久久精品久久精品一区二区三区| 女人久久www免费人成看片| 啦啦啦视频在线资源免费观看| 国产欧美亚洲国产| 亚洲精品av麻豆狂野| 少妇高潮的动态图| 精品少妇内射三级| 免费观看在线日韩| 大香蕉97超碰在线| 亚洲av欧美aⅴ国产| 欧美xxxx性猛交bbbb| 汤姆久久久久久久影院中文字幕| 超碰97精品在线观看| 欧美人与性动交α欧美精品济南到 | 久久精品久久久久久噜噜老黄| 内地一区二区视频在线| 亚洲综合色惰| 大又大粗又爽又黄少妇毛片口| 国产精品免费大片| 亚洲无线观看免费| 啦啦啦视频在线资源免费观看| 国产白丝娇喘喷水9色精品| 欧美日韩国产mv在线观看视频| 中文字幕人妻丝袜制服| 色婷婷久久久亚洲欧美| 这个男人来自地球电影免费观看 | 亚洲国产精品专区欧美| 夜夜爽夜夜爽视频| 伦理电影免费视频| 日韩,欧美,国产一区二区三区| 午夜福利视频精品| 午夜福利在线观看免费完整高清在| 国产成人a∨麻豆精品| 一本一本久久a久久精品综合妖精 国产伦在线观看视频一区 | 少妇 在线观看| 久久久久久久久久人人人人人人| 美女xxoo啪啪120秒动态图| 男人添女人高潮全过程视频| 国产精品秋霞免费鲁丝片| 亚洲成人一二三区av| 亚洲av欧美aⅴ国产| 最近手机中文字幕大全| av有码第一页| 久久人人爽av亚洲精品天堂| 久久精品熟女亚洲av麻豆精品| 亚洲国产av影院在线观看| 美女视频免费永久观看网站| 婷婷色麻豆天堂久久| 老司机影院成人| 大陆偷拍与自拍| 日日爽夜夜爽网站| a级片在线免费高清观看视频| 男女边吃奶边做爰视频| 欧美精品高潮呻吟av久久| 少妇人妻久久综合中文| 久久久久久久国产电影| 最黄视频免费看| 久久精品国产a三级三级三级| 亚州av有码| av视频免费观看在线观看| 综合色丁香网| 99国产综合亚洲精品| 亚洲国产色片| 国产精品一国产av| 中文字幕人妻熟人妻熟丝袜美| 国产一区亚洲一区在线观看| 国产精品人妻久久久久久| 天美传媒精品一区二区| 亚洲成人av在线免费| 曰老女人黄片| 日本av免费视频播放| 国产深夜福利视频在线观看| 满18在线观看网站| 天美传媒精品一区二区| 一本一本久久a久久精品综合妖精 国产伦在线观看视频一区 | 大话2 男鬼变身卡| 建设人人有责人人尽责人人享有的| 只有这里有精品99| 久久精品国产亚洲网站| 国产极品天堂在线| a级毛片黄视频| 黄色一级大片看看| 国产精品一区二区在线不卡| 亚洲第一av免费看| 日韩精品有码人妻一区| 另类精品久久| 欧美精品人与动牲交sv欧美| 国内精品宾馆在线| 成人亚洲欧美一区二区av| 高清不卡的av网站| 精品少妇黑人巨大在线播放| 国产精品一区二区在线不卡| 两个人免费观看高清视频| 大码成人一级视频| 十八禁高潮呻吟视频| 最新中文字幕久久久久| 嘟嘟电影网在线观看| 国产精品成人在线| 少妇 在线观看| 男的添女的下面高潮视频| 中文字幕精品免费在线观看视频 | 国产女主播在线喷水免费视频网站| 热99国产精品久久久久久7| 免费观看在线日韩| 大香蕉97超碰在线| 国产成人av激情在线播放 | 久久综合国产亚洲精品| 欧美日韩综合久久久久久| 九色亚洲精品在线播放| 3wmmmm亚洲av在线观看| 久久久久久久亚洲中文字幕| 中国国产av一级| 日本黄色日本黄色录像| 99久国产av精品国产电影| 蜜臀久久99精品久久宅男| 国产爽快片一区二区三区| 欧美精品国产亚洲| 一本一本久久a久久精品综合妖精 国产伦在线观看视频一区 | 一区在线观看完整版| 在线观看人妻少妇| 国产视频首页在线观看| .国产精品久久| 久久久久精品性色| 欧美bdsm另类| 久久狼人影院| 国产高清有码在线观看视频| 欧美xxⅹ黑人| 丝袜在线中文字幕| 国产一区亚洲一区在线观看| 欧美人与善性xxx| 亚洲综合精品二区| 91国产中文字幕| 国产乱人偷精品视频| 精品一区在线观看国产| 亚洲人成77777在线视频| 日韩 亚洲 欧美在线| 久久久久精品久久久久真实原创| 在线观看免费视频网站a站| av国产精品久久久久影院| 三级国产精品欧美在线观看| 国产一级毛片在线| 中文精品一卡2卡3卡4更新| 久久精品国产亚洲av涩爱| 亚洲四区av| 国产精品一国产av| 国产免费又黄又爽又色| 久久99热这里只频精品6学生| 国产成人精品久久久久久| 性色av一级| 97超视频在线观看视频| 18+在线观看网站| 亚洲婷婷狠狠爱综合网| 18在线观看网站| 新久久久久国产一级毛片| 久久ye,这里只有精品| 老司机影院毛片| 一边摸一边做爽爽视频免费| av免费在线看不卡| 久久久精品免费免费高清| 最近中文字幕高清免费大全6| 在线看a的网站| 欧美 日韩 精品 国产| 国产亚洲欧美精品永久| 国产精品一区二区三区四区免费观看| a 毛片基地| 99热国产这里只有精品6| 久久这里有精品视频免费| 高清不卡的av网站| 国产精品 国内视频| av线在线观看网站| 国产片内射在线| 99国产综合亚洲精品| 久久久精品区二区三区| 黑人高潮一二区| 久久鲁丝午夜福利片| 午夜激情av网站| 又大又黄又爽视频免费| 日本欧美视频一区| 啦啦啦视频在线资源免费观看| 天天躁夜夜躁狠狠久久av| 久久久久久久久久久丰满| 亚洲av电影在线观看一区二区三区| 欧美日韩一区二区视频在线观看视频在线| 超碰97精品在线观看| 一级片'在线观看视频| 国产精品.久久久| 美女国产高潮福利片在线看| 亚洲av成人精品一二三区| 亚洲精华国产精华液的使用体验| 婷婷色综合www| av免费观看日本| 日韩一区二区视频免费看| 久久午夜福利片| 日本与韩国留学比较| 少妇精品久久久久久久| 亚洲,一卡二卡三卡| 日韩av在线免费看完整版不卡| 国产日韩欧美亚洲二区| 午夜免费鲁丝| 母亲3免费完整高清在线观看 | 精品少妇内射三级| 只有这里有精品99| 夜夜爽夜夜爽视频| 热re99久久国产66热| 久久久午夜欧美精品| 亚洲av成人精品一区久久| 一二三四中文在线观看免费高清| 视频在线观看一区二区三区| 国产色爽女视频免费观看| 国精品久久久久久国模美| 亚洲av中文av极速乱| 国产一区有黄有色的免费视频| 高清不卡的av网站| 最新的欧美精品一区二区| 国产成人精品一,二区| 国精品久久久久久国模美| 国产黄色免费在线视频| 天天影视国产精品| 嫩草影院入口| 国产一区二区三区av在线| 99久久精品一区二区三区| 日韩免费高清中文字幕av| 国产一区二区在线观看日韩|