Growth simulation and yield prediction for perennial jujube fruit tree by integrating age into the WOFOST model

Mathematical models have been widely employed for the simulation of growth dynamics of annual crops,thereby performing yield prediction,but not for fruit tree species such as jujube tree(Zizyphus jujuba).The objectives of this study were to investigate the potential use of a modified WOFOST model for predicting jujube yield by introducing tree age as a key parameter.The model was established using data collected from dedicated field experiments performed in 2016-2018.Simulated growth dynamics of dry weights of leaves,stems,fruits,total biomass and leaf area index(LAI) agreed well with measured values,showing root mean square error(RMSE) values of 0.143,0.333,0.366,0.624 t ha^-1 and 0.19,and R2 values of 0.947,0.976,0.985,0.986 and 0.95,respectively.Simulated phenological development stages for emergence,anthesis and maturity were 2,3 and 3 days earlier than the observed values,respectively.In addition,in order to predict the yields of trees with different ages,the weight of new organs(initial buds and roots) in each growing season was introduced as the initial total dry weight(TDWI),which was calculated as averaged,fitted and optimized values of trees with the same age.The results showed the evolution of the simulated LAI and yields profiled in response to the changes in TDWI.The modelling performance was significantly improved when it considered TDWI integrated with tree age,showing good global(R2≥0.856,RMSE≤0.68 t ha^-1) and local accuracies(mean R2≥0.43,RMSE≤0.70 t ha^-1).Furthermore,the optimized TDWI exhibited the highest precision,with globally validated R2 of 0.891 and RMSE of 0.591 t ha^-1,and local mean R2 of 0.57 and RMSE of 0.66 t ha^-1,respectively.The proposed model was not only verified with the confidence to accurately predict yields of jujube,but it can also provide a fundamental strategy for simulating the growth of other fruit trees.

An imputation/copula-based stochastic individual tree growth model for mixed species Acadian forests： a case study using the Nova Scotia permanent sample plot network

Background： A novel approach to modelling individual tree growth dynamics is proposed. The approach combines multiple imputation and copula sampling to produce a stochastic individual tree growth and yield projection system. Methods： The Nova Scotia, Canada permanent sample plot network is used as a case study to develop and test the modelling approach. Predictions from this model are compared to predictions from the Acadian variant of the Forest Vegetation Simulator, a widely used statistical individual tree growth and yield model. Results： Diameter and height growth rates were predicted with error rates consistent with those produced using statistical models. Mortality and ingrowth error rates were higher than those observed for diameter and height, but also were within the bounds produced by traditional approaches for predicting these rates. Ingrowth species composition was very poorly predicted. The model was capable of reproducing a wide range of stand dynamic trajectories and in some cases reproduced trajectories that the statistical model was incapable of reproducing. Conclusions： The model has potential to be used as a benchmarking tool for evaluating statistical and process models and may provide a mechanism to separate signal from noise and improve our ability to analyze and learn from large regional datasets that often have underlying flaws in sample design.

Development and evaluation of an individual tree growth and yield model for the mixed species forest of the Adirondacks Region of New York, USA

Background： Growth and yield models are important tools for forest planning. Due to its geographic location, topology, and history of management, the forests of the Adirondacks Region of New York are unique and complex. However, only a relatively limited number of growth and yield models have been developed and/or can be reasonably extended to this region currently. Methods： in this analysis, 571 long-term continuous forest inventory plots with a total of 10 - 52 years of measurement data from four experimental forests maintained by the State University of New York College of Environmental Science and Forestry and one nonindustrial private forest were used to develop an individual tree growth model for the primary hardwood and softwood species in the region. Species-specific annualized static and dynamic equations were developed using the available data and the system was evaluated for long-term behavior. Results： Equivalence tests indicated that the Northeast Variant of the Forest Vegetation Simulator （FVS-NE） was biased in its estimation of tree total and bole height, diameter and height increment, and mortality for most species examined. In contrast, the developed static and annualized dynamic, species-specific equations performed quite well given the underlying variability in the data. Long-term model projections were consistent with the data and suggest a relatively robust system for prediction. Conclusions： Overall, the developed growth model showed reasonable behavior and is a significant improvement over existing models for the region. The model also highlighted the complexities of forest dynamics in the region and should help improve forest planning efforts there.

A Spatially Heterogeneous Expert Based (SHEB) Urban Growth Model using Model Regionalization

Urbanization changes have been widely examined and numerous urban growth models have been proposed. We introduce an alternative urban growth model specifically designed to incorporate spatial heterogeneity in urban growth models. Instead of applying a single method to the entire study area, we segment the study area into different regions and apply targeted algorithms in each subregion. The working hypothesis is that the integration of appropriately selected region-specific models will outperform a globally applied model as it will incorporate further spatial heterogeneity. We examine urban land use changes in Denver, Colorado. Two land use maps from different time snapshots (1977 and 1997) are used to detect the urban land use changes, and 23 explanatory factors are produced to model urbanization. The proposed Spatially Heterogeneous Expert Based (SHEB) model tested decision trees as the underlying modeling algorithm, applying them in different subregions. In this paper the segmentation tested is the division of the entire area into interior and exterior urban areas. Interior urban areas are those situated within dense urbanized structures, while exterior urban areas are outside of these structures. Obtained results on this model regionalization technique indicate that targeted local models produce improved results in terms of Kappa, accuracy percentage and multi-scale performance. The model superiority is also confirmed by model pairwise comparisons using t-tests. The segmentation criterion of interior/exterior selection may not only capture specific characteristics on spatial and morphological properties, but also socioeconomic factors which may implicitly be present in these spatial representations. The usage of interior and exterior subregions in the present study acts as a proof of concept. Other spatial heterogeneity indicators, for example landscape, socioeconomic and political boundaries could act as the basis for improved local segmentations.

STUDY ON THE TREE GROWTH, ARCHITECTURE AND STAND STRUCTURE OF KOREAN PINE PLANTATION

The artificial pure and mixed Korean pine (Pinus koraiensis) forests were investigated at Dailing Forestry Bureau in Xiaoxing'an mountains from 1990 to 1992. Depending on the distance between the samplings of Korean pine and their neighbor trees, the neighbor tree height, the size of neighbor tree canopy, and dimension of neighbor tree. The forest structure was classified into three types: (1) prowth of a tree in the light (open), (2) Growth of a tree in the canopy gap (Gap), (3)Growth of a tree under broad-leaved tree canopy. The frequeney, height, and age of stem divergence of Korean pine tree were investigated by sampling trees. The temporal and spatial model of the tree growth was applied on basis of the height of stem divergence, ratio of height and DBH, and character of tree stem.The morphology and growth character of Korean pine trees during different development stage were forecasted.

Estimating Pinus palustris tree diameter and stem volume from tree height,crown area and stand-level parameters

Accurate and efficient estimation of forest growth and live biomass is a critical element in assessing potential responses to forest management and environmental change. The objective of this study was to develop models to predict longleaf pine tree diameter at breast height （dbh） and merchantable stem volume （V） using data obtained from field measurements. We used longleaf pine tree data from 3,376 planted trees on 127 permanent plots located in the U.S. Gulf Coastal Plain region to fit equations to predict dbh and V as functions of tree height （H） and crown area （CA）. Prediction of dbh as a function of H improved when CA was added as an additional independent variable. Similarly, predic- tions of V based on H improved when CA was included. Incorporation of additional stand variables such as age, site index, dominant height, and stand density were also evaluated but resulted in only small improvements in model performance. For model testing we used data from planted and naturally-regenerated trees located inside and outside the geographic area used for model fitting. Our results suggest that the models are a robust alternative for dbh and V estimations when H and CA are known on planted stands with potential for naturally-regenerated stands, across a wide range of ages. We discuss the importance of these models for use with metrics derived from remote sensing data.

Current Networks of Long Proxies for Building Reconstruction Models of the Atlantic Multidecadal Oscillation

Currently available proxies were studied as networks for building reconstruction models of the Atlantic Multidecadal Oscillation (AMO). Only proxies that would double the current record length (backwards in time from AD 1564) were included. We present two proxy networks and corresponding reconstruction (transfer) models, one for tree-growth based proxies only and another for multiproxies. Both of them show a useful match in timing as well as amplitude with the AMO. These model structures demonstrated reasonable model performance (overall r2 = 0.45 - 0.36). The time stability of proxy-AMO relationships was also validated. The new models produced acceptable results in cross-calibration-verification (reduction of error and coefficient of efficiency statistics in 1856-1921 and 1922-1990 vary between 0.41 and 0.21). The spatial distribution of these data series indicate that proxies respond to an AMO-like climatic oscillation over much of the Northern Hemisphere.

基于GA-BP神经网络的新疆南疆核桃树生长模型研究

文章提出了一种利用遗传算法优化BP神经网络的核桃树生长模型来预测核桃树的树高、胸径的方法,通过优化BP神经网络的权值和阈值建立GA-BP模型,与多元线性回归模型的预测结果进行比较。结果表明:采用遗传算法优化后的模型具有更高的预测精度,对核桃树生长预测具有指导意义。

青海省不同生境下青海云杉胸径生长模型研究

[目的]研究不同生境压力下青海云杉的林木胸径生长规律及生长模型,为有效保护、合理经营青海云杉林提供经验模型。[方法]利用青海地区青海云杉的树轮数据,计算单木胸径生长量,分析不同起源、不同坡位条件下胸径生长规律,构建单木胸径生长模型,对比与评价不同模型的拟合优度结果。随后选取基础模型,建立考虑起源和坡位的青海云杉单木胸径混合效应模型,采用全部数据对模型进行检验。[结果]总体来看,青海云杉生长到胸高位置后,单木胸径生长量随着年龄的增加呈现下降后平缓变化趋势;青海云杉天然林、人工林单木生长的速生期分别为29—44 a、29—39 a,连年生长量(CAI)和平均生长量(MAI)均在0.40 cm以上,随后天然林单木CAI和MAI的变化平缓,人工林的变化幅度较大。不同坡位的单木胸径生长趋势具有差异。生长模型结果显示,不同起源、坡位条件下各树种最优胸径生长模型的决定系数(R^(2))均在0.913以上,总体相对误差(TRE)和平均系统误差(MSE)均在±2%以内,平均预估误差(MPE)大多在5%以内,平均百分标准误差(MPSE)在35%以内。以Gompertz模型为基础模型构建的混合效应模型的R^(2)为0.702,拟合效果优于基础模型;检验指标TRE为0.03%,MSE为-0.30%,MPE为4.23%,MPSE为29.54%,较基础模型分别下降89.3%、83.5%、20.6%、15.1%。[结论]不同生境条件下青海云杉的胸径生长规律具有差异,天然林的快速生长期持续时间长;所构建的单木胸径生长混合效应模型,可以用于估算青海省不同生境条件下青海云杉的林木胸径生长量变化。

Which trees should be removed in thinning treatments?

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 managemen 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.

邻近木多样性与竞争对天然云冷杉林树木生长的影响

【目的】探究天然云冷杉林邻近木多样性、竞争强度对林木生长的影响,为云冷杉林结构化经营和管理提供科学依据。【方法】选取长白山天然云冷杉林2015、2018年两期固定样地调查数据,基于结构方程模型分析邻近木多样性、竞争与树木生长的关系。【结果】(1)研究区内天然云冷杉林的邻近木树种、径阶、树高多样性指数均集中在1.04处,3个多样性指数整体上分布均匀,树种混交度高,林分结构复杂。(2)结构方程模型中,树种、径阶、树高3个邻近木多样性指数和竞争指数对材积生长量的总影响系数分别为-0.001、0.166、0.073和-0.489,结果表明竞争是影响林木生长的关键因素。(3)径阶和树高多样性的增加对生长量均为正面影响,其中径阶多样性为直接影响,树高多样性为间接影响;树种多样性表现为直接的负面影响与间接的正面影响,总体为负面影响;此外,树种多样性的提高可以减少林木间的竞争强度,树高多样性的提高可能会导致林分结构的分化,进而促进林分中林木个体的生长。(4)研究区内林木的生长压力可能多来自于同径级林木,小径级林木生长状况较差且竞争压力较大,大中径级林木与之相反。【结论】择伐同径级或相近径级林木,同时提高林分内的径阶、树高多样性水平,可以降低林木竞争水平,促进林木个体生长,进而提高云冷杉林林分生产力。

天然云冷杉林树木生长与树木大小、竞争和树种多样性的关系

【目的】林分内林木大小、竞争和树种多样性等多方面因素影响着林木的生长,而胸高断面积生长量通常被用来描述树木生长状态。本文利用长白山云冷杉针阔混交林主要树种的单木胸高断面积生长量建立随机森林模型,研究和量化影响树木生长的环境因素,旨在为该地区的云冷杉针阔混交林生长预估提供理论依据。【方法】连续24年(1987—2010年)对总样木数为6903株的固定样地进行数据调查,应用随机森林算法,选取单木、竞争因子、多样性和气候方面共11个调查因子,对混交林中6个主要树种建立胸高断面积生长量模型,并使用10折交叉验证法来优化超参数mtry和评估模型结果。【结果】(1)臭冷杉、云杉、红松、椴树、枫桦、白桦6个主要树种胸高断面积生长量模型的决定系数分别为0.663、0.683、0.695、0.459、0.384和0.568。(2)单木胸高断面积是最重要的因子,对树木生长有着很强的正向影响;竞争因子和树木大小多样性是影响树木生长的主要因素,单木胸高断面积生长量随着竞争因子、树木大小多样性增加而下降。(3)树种多样性对树木生长的影响比较有限,树种多样性指数和混交度的增加会一定程度加快云杉、臭冷杉和红松的生长速度;而气候因子对树木生长的影响则相对较小。【结论】树木生长在很大程度上依赖于其自身的生长潜力,在外界环境中主要受到来自林木间竞争和树木大小多样性的抑制,而树种多样性的增加也能在一定程度促进林内优势树种的生长;随机森林模型能够很好地量化和显出各变量与单木胸高断面积生长量之间复杂的关系,可以作为森林管理评价工具,为森林生长收获预估提供新的方法。

应用优化建模法构建红松人工林单木直径生长模型

为了提高不完备数据集下红松人工林直径生长模型的预测精度,以1980—2022年黑龙江省75块红松林不同复测间隔期的样地数据(林木胸径、竞争因子和立地等),利用优化建模法构建红松人工林单木直径生长模型,采用单纯形优化算法寻求模拟和实测直径分布差异最小下的模型参数,并利用自举法对模型进行检验。结果表明:优化建模法损失函数的初始参数a=1、b=0.003、c1=c2=1.5时,预测期末林分断面积偏差为0.02 m^(2)·hm^(-2),显著优于由传统归回建模法的精度(林分断面积偏差为0.44 m^(2)·hm^(-2));结合实测数据与视图分析,验证了优化建模法的优势和可靠性,揭示了优化法损失函数与传统回归法构建的直径生长模型之间的差异。因此,优化法损失函数构建的红松人工林生长模型在模拟自然生长中表现优越,为制定科学的森林经营方案提供了技术支持。

人为管护因素哑变量对行道树国槐胸径-树高模型精度的影响——以北京市中心城区为例

为明晰特大城市都市区城市树木人为管护因素对行道树国槐的胸径-树高关系的影响,拟构建以人为管护因素为哑变量的行道树国槐胸径-树高关系模型,根据2022年北京市中心城区行道树的抽样调查,取得685株行道树国槐的实测数据,结合文献中的12个模型,通过决定系数(R2)、平均预估误差(MPE)、赤池信息准则(AIC)、均方根误差(RMSE)4个评价指标,筛选出最优基础模型,分析7种典型城市环境中的人为管护因素对行道树国槐胸径、树高的影响。结果表明:1)模型M2(R2=0.647,MPE=-2.241%,AIC=600.726,RMSE=1.683)综合拟合程度最优。2)地面防踩踏护网设置、建筑遮光和树池边长显著影响国槐胸径(P<0.05);涂白保护、地面防踩踏护网设置和建筑遮光情况、垂直结构以及树池边长显著影响国槐树高(P<0.05);株距和邻近树种的差异对国槐的胸径、树高生长影响不显著(P>0.05)。3)在模型中引入代表地面防踩踏保护、树木涂白、建筑遮光和树池边长的哑变量后,模型精度明显提升,R2提升15.8%,MPE缩小29.5%,AIC值降低29.0%,RMSE值降低15.0%。因此,将人为管护因素作为哑变量引入国槐胸径-树高模型可以有效提高模型的拟合精度与适用性,可为未来行道树的科学栽植、管护以及碳储量评估等提供理论基础。

基于随机森林算法的桉树人工林单木生物量预估模型

单木生物量模型是估测森林生物量的基础。通过标准木法实测雷州半岛地区90株桉树单株生物量数据,随机划分60个样本数据作为训练集,30个样本数据作为验证集。以林龄、树高和胸径为自变量,单木生物量为因变量,使用岭回归模型、异速生长模型和随机森林算法构建模型,采用决定系数(R^(2))、均方根误差(RMSE)和平均绝对误差(MAE)对模型进行评价。结果表明:随机森林模型的R~2、RMSE和MAE无论在训练集还是验证集均高于岭回归模型和异速生长模型。由随机森林模型的因子重要值可知,胸径是影响单木生物量的主要因子。引入林龄因子后的随机森林模型可以提高单木生物量的预测精度,为碳汇计量提供基础数据和模型支撑。

“天潜沔”地区杨树人工林胸径-树高生长模型研究

以天门、潜江、仙桃地区杨树人工林为研究对象,通过2022年在该地区选取48块样地进行每木检尺,共测量731株杨树的生长量情况,作为建模数据。利用R软件对胸径-树高模型进行拟合,选用9个常用的胸径-树高模型。根据决定系数(R^(2))、均方根误差(RMSE)、残差平均和(SSE)、平均绝对误差(MAE)、平均相对误差(MRE)和Akaike信息量准则(AIC)6个评价指标来确定最优模型,同时以天门、潜江、仙桃地区2021年立地条件、初植密度基本一致的10个杨树人工林固定样地的胸径、树高数据为检验数据,检验最优模型的预测能力。结果表明:在1~9号模型中,4号拟合效果最佳,其模型表达式为H=1.0410D-0.0122D^(2),可以作为天门、潜江、仙桃地区杨树人工林胸径-树高基础模型。

宁夏六盘山北部华北落叶松林树高与胸径生长的多因子响应耦合模型构建

【目的】分析宁夏六盘山半干旱区叠叠沟小流域华北落叶松人工林林龄、密度和主要立地因子对树高和胸径生长的影响,建立树木生长的多因子响应耦合模型,为该地区华北落叶松人工林生长预测和可持续经营提供理论依据。【方法】2018和2019年,在六盘山半干旱区叠叠沟小流域分别选择42和57块华北落叶松人工林样地,调查树高、胸径、林龄、密度等林分结构特征以及坡向、土壤厚度等立地因子,并收集2003—2017年在叠叠沟小流域调查的93块样地历史数据。基于192块样地数据,分析树高和胸径对林龄、密度、坡向、土壤厚度等因子的响应规律,利用2/3和1/3的数据建立和验证树高与胸径生长的多因子响应耦合模型。【结果】树高和胸径对林龄、密度、坡向、土壤厚度的响应规律各异;树高和胸径随林龄增加先快速增加,后在林龄超过20年时缓慢增加;树高和胸径随密度增加先表现为稳定,后在密度分别超过2000、1500株·hm^(-2)时缓慢下降,在密度超过3000、2300株·hm^(-2)时加快下降;树高和胸径随坡向偏离正北方向的角度增加先表现为稳定,后在坡向超过80°时迅速降低;树高和胸径随土壤厚度增加先表现为增加,后在土壤厚度分别超过90、80 cm渐趋稳定。基于连乘耦合、立地指数和立地因子3种林木生长模型表达式,建立树高和胸径响应林龄、密度、坡向、土壤厚度和立地指数的耦合模型并拟合模型参数;树高模型的确定系数分别为0.804、0.902、0.806,NS系数分别为0.793、0.903、0.762;胸径模型的确定系数分别为0.846、0.803、0.834,NS系数分别为0.837、0.840、0.818;3种模型拟合效果均较好。【结论】宁夏六盘山半干旱区华北落叶松人工林树高和胸径生长随林龄、密度、坡向、土壤厚度增加分别表现为先快后慢增加、先稳定后下降、先稳定后降低、先升高后稳定,建立的连乘耦合、立地指数和立地因子多因子耦合模型精度均较高,但鉴于研究区尚未确定立地指数分布规律并综合考虑各模型精度、应用难度、结构复杂程度差异,建议采用多因子连乘耦合模型预测华北落叶松人工林树高和胸径生长。

湖南本土碳汇树种观光木地上生物量模型研究

以生长因子、树干鲜质量、树木枝鲜质量、叶鲜质量等相关测树因子为自变量,建立湖南省本土珍稀树种观光木各生物量组分及地上部分生物量模型,旨在为筛选湖南省本土碳汇树种、探索其生物量及碳储量变化规律提供理论和实践依据。本研究基于5个径阶15株伐倒木的叶、茎、枝生物量数据,对胸径、树高、冠幅、树冠体积进行测算,运用多种函数和不同自变量建立生物量模型,筛选出拟合效果最优及相关性最高的模型作为最佳生物量估算模型。结果显示:干、枝及地上部分最优生物量模型为以地径D和株高h为自变量二元幂函数[干和地上部分:W=a+b(D^(2)h);枝:W=a(D^(2)h)^(b)],模型拟合判断系数R^(2)相对较高,具有较好的拟合精度和预估水平。

缺血性脑卒中偏瘫患者康复期运动恐惧变化轨迹及核心影响因素决策树分析

目的:分析缺血性脑卒中偏瘫患者康复期运动恐惧变化轨迹及其核心影响因素。方法:采用便利抽样法选取158例缺血性脑卒中偏瘫患者为研究对象。采用心脏病患者运动恐惧量表分别在出院当天,出院后1、3、6个月评估其运动恐惧水平,采用潜类别模型对潜在类别进行分类,通过决策树模型分析康复期运动恐惧轨迹潜在类别的核心因素。结果:缺血性脑卒中偏瘫患者康复期运动恐惧变化轨迹分为2个潜在类别:高运动恐惧-缓慢降低组(31.01%)和中运动恐惧-快速降低组(68.99%)。自我效能得分、脑卒中复发次数、合并症、家庭关怀度得分、康复运动指导、跌倒史是影响康复期运动恐惧轨迹亚组的影响因素,其中自我效能得分是最为重要的影响因素,信息增益为0.312。结论:缺血性脑卒中偏瘫患者康复期运动恐惧呈不同变化轨迹,医务人员可制定以改善自我效能得分为主的相关干预方式用以改善患者运动恐惧水平。

内蒙古大兴安岭林区白桦天然林单木胸径生长模型构建

【目的】为准确预测内蒙古大兴安岭林区的白桦天然林单木生长并制定适应性经营管理措施,构建包含气候因子和林木大小多样性因子的白桦单木胸径生长混合效应模型。【方法】数据来源于内蒙古大兴安岭林区第8和9次森林资源清查数据,共筛选出符合要求的固定样地97块,白桦样木4785株。基于筛选的数据,运用逐步回归法,构建传统单木胸径生长模型,并在此模型的基础上,引入样地随机效应,构建白桦单木胸径生长混合效应模型。利用十折交叉验证法分别对已构建的基础模型和混合效应模型进行检验。【结果】期初胸径的对数、大于对象木的断面积和、样地内胸径标准差、生长积温和年平均降雨量对白桦单木胸径生长量存在显著影响。与基础模型相比,混合效应模型的调整决定系数提高了0.1206,均方根误差降低了0.0971 cm2,模型的预测精度显著提高。十折交叉验证的结果表明混合效应模型同样表现出较好的拟合结果。【结论】包含气候因子和林木大小多样性因子的白桦单木胸径生长混合效应模型能精准预测白桦单木胸径的生长动态,可以为内蒙古大兴安岭林区的白桦天然林的科学经营提供支持。