Agglomeration, inequality, and the environment in an R&D-based growth model

The composition and dynamics of plant communities arise from individual-level demographic outcomes, which are driven by interactions between phenotypes and the environment. Functional traits that can be measured across plants are frequently used to model plant growth and survival. Perhaps surprisingly, species average trait values are often used in these studies and, in some cases, these trait values come from other regions or averages calculated from global databases. This data aggregation potentially results in a large loss of valuable information that probably results in models of plant performance that are weak or even misleading. We present individual-level trait and fine-scale growth data from >500 co-occurring individual trees from 20 species in a Chinese tropical rain forest. We construct Bayesian models of growth informed by theory and construct hierarchical Bayesian models that utilize both individual- and species-level trait data, and compare these models with models only using individual-level data. We show that trait-growth relationships measured at the individual level vary across species, are often weak using commonly measured traits and do not align with the results of analyses conducted at the species level. However, when we construct individual-level models of growth using leaf area ratio approximations and integrated phenotypes, we generated strong predictive models of tree growth. Here, we have shown that individual-level models of tree growth that are built using integrative traits always outperform individual-level models of tree growth that use commonly measured traits. Furthermore, individual-level models, generally, do not support the findings of trait-growth relationships quantified at the species level. This indicates that aggregating trait and growth data to the species level results in poorer and probably misleading models of how traits are related to tree performance.

The study of growth model is a basic research in forest growth and yield modeling. Most of growth models were developed using ordinary regression method. It is assumed that the observations were independent and obey Gauss distribution. Those models reflect the average growth across different plots, but neglect the correlation and variance between individuals and plots. However, mixed-effects models which include both fixed and random parameters could solve the problem very well. Our objective is to develop growth models for main tree species spruce in Changbai Mountains by using mixed-effects models. 14 clear cutting plots were investigated in Changbai Mountain area, China. Data of 619 individual trees including age, height, diameter, and volume were used in this study to calibrate the growth models. Richards model and its mixed-effects model were used to develop growth models by using PROC NLIN and PROC NLMIXED in SAS. Decision coefficient (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ), root mean square error (RMSE), and mean absolute difference (MAD) were used to evaluate the accuracy of the two models. Compared with the basic model, the R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of the mixed-effects model which included random-effect parameter increased 37%-82%, RMSE and MAD decreased 12%-30% and 13-28%, respectively. In conclusion, the mixed-effects model was suitable to calibrate growth models.

For decades, the entrepreneur was considered the" invisible man".The role of entrepreneurship was ignored in economic literature until the 20th century and in economic growth and development models until the early the 1990s.Since then, the role of entrepreneur is included in different branches of economics.The purpose of this paper is to review the role of the entrepreneurs and entrepreneurship in economic growth and development models.After being considered an invisible man in the economics literature, Schumpeter 1911, 1934 considered economic development as a dynamic process that disturbs the closed circular flow of the economic system.Schumpeter considered the entrepreneur as an innovator who plays a fundamental role in the process of economic development.However, few attempts have been made to include entrepreneurship in neoclassical and general equilibrium growth and development models due to the assumptions of perfect competition, static market equilibrium and perfect information about markets and production processes.Endogenous growth models (Romer 1986(Romer , 1990;; Lucas 1990;Schmitz 1989;Aghion and Howitt 1992) make it possible to include entrepreneurship and entrepreneurs in growth models by emphasizing the role of knowledge externalities, innovations and returns to scale.They consider entrepreneurship as the channel through which these processes affect economic growth.The paper also reviews empirical studies using three macroeconomics databases to describe the entrepreneurial impact on growth, productivity and employment.

Latent growth models make up a class of methods to study within-person change-how it progresses, how it differs across individuals, what are its determinants, and what are its consequences. Latent growth methods have been applied in many domains to examine average and differential responses to interventions and treatments. In this review, we introduce the growth modeling approach to studying change by presenting different models of change and interpretations of their model parameters. We then apply these methods to examining sex differences in the development of binge drinking behavior through adolescence and into adulthood. Advances in growth modeling methods are then discussed and include inherently nonlinear growth models, derivative specification of growth models, and latent change score models to study stochastic change processes. We conclude with relevant design issues of longitudinal studies and considerations for the analysis of longitudinal data.

In Komatiland Forests (South Africa) the objectives of growth and yield research are to develop a range of empirical stand-level growth and yield models for thinned pine stands. The models are used for resource description, growth and yield predictions for the purpose of management planning and for the development of optimum stand-level management regimes. The growth and yield research programme consists of four main components, namely a field programme, long term data management, growth modelling and the development of simulation systems. For each of these components, a strategy has been developed. In the field, programme data are collected for each of the target model components from a number of sources, such as spacing trials, permanent sample plots (PSPs), silvicultural response trials, plantation inventories and destructive sampling for volume and taper data. The modelling strategy is to develop multi-component model architectures for modelling stand-level growth, namely dominant height, unthinned basal area and survival. For thinned stands, additional functions for the modelling of the basal area thinning ratio and the basal area response after thinning are required. Depending on the data, both the Chapman-Richards-type and the Schumacher-type functions are used for modelling unthinned stand-level basal area. Various methods are used for modelling basal area response after thinning, e.g. the index of suppression or the age-adjustment method. A major challenge in growth modelling is to develop models for thinned stands based on PSP data only, while conforming to the hypothesis of thinned stand growth. Stand structure is modelled using the Weibull function in conjunction with the method of moments approach for recovering the relevant Weibull parameters. Finally, the models are built into simulation systems for stand-level scenario analysis, e.g. the development of rule-based thinning and pruning systems, and also forestry scenario analysis. However, the main function of stand-level growth and yield models is still to provide for more accurate company planning systems.

Models that predict forest development are essential for sustainable forest management. Constructing growth models via regression analysis or fitting a family of sigmoid equations to construct compatible growth and yield models are two ways these models can be developed. In this study, four species-specific models were developed and compared. A compatible growth and yield stand basal area model and a five-year stand basal area growth model were developed for Scots pine ( L.) and Norway spruce ( (L.) Karst.). The models were developed using data from permanent inventory plots from the Swedish national forest inventory and long-term experiments. The species-specific models were compared, using independent data from long-term experiments, with a stand basal area growth model currently used in the Swedish forest planning system Heureka (Elfving model). All new models had a good, relatively unbiased fit. There were no apparent differences between the models in their ability to predict basal area development, except for the slightly worse predictions for the Norway spruce growth model. The lack of difference in the model comparison showed that despite the simplicity of the compatible growth and yield models, these models could be recommended, especially when data availability is limited. Also, despite using more and newer data for model development in this study, the currently used Elfving model was equally good at predicting basal area. The lack of model difference indicate that future studies should instead focus on model development for heterogeneous forests which are common but lack in growth and yield modelling research.Pinus sylvestrisPicea abies

We examined currently available empirical growth models which could be potentially applicable to coppice growth and production modelling. We compiled a summary of empirical models applied in coppices, high forests and fast-growing tree plantations, including coppice plantations. The collected growth models were analysed in order to find out whether they encompassed any of 13 key dendrometric and structural variables that we found as characteristic for coppices. There is no currently available complex growth model for coppices in Europe. Furthermore, many aspects of coppice growth process have been totally ignored or omitted in the most common modelling approaches so far. Within-stool competition, mortality and stool morphological variability are the most important parameters. However, some individual empirical submodels or their parts are potentially applicable for coppice growth and production modelling (e. g. diameter increment model or model of resprouting probability). As the issue of coppice management gains attention, the need for a decision support tool (e.g. coppice growth simulator) becomes more actual.

نظريات ونماذج النمو | الاقتصاد الإسلامي | الاشتراكية والرأسمالية | الأزمات الاقتصادية | الدول النامية | Theories and Models of Growth | Islamic Economics | Socialism and Capitalism | Economic Crises | Developing Countries

The problem that this study deals with is ontogenetic growth of humans and animals. The novelty of this research is an approach to the problem how to recognise growth phenotypes in animals. The aim of this research was to analyse an analytical model of ontogenetic growth of animals with intention to recognise growth phenotypes. In this study we discuss possibility to extend results to the modelling of growth phenotypes in humans. In this study we not only analysed the model of animal growth but also offer an insight into the option to apply some methods known in mathematical physics and applied mathematics. In this research we concentrate on the modelling of growth of pigs. Pigs are known as a good model animal of humans in many aspects, including growth, obesity, digestion, and some others. In this model two aspects of ontogenetic growth were considered; the intention was to advance the biological understanding of the growth process.

Human Body Shape Index Based on an Experimentally Derived Model of Human Growth

Fault growth is widely described using a scaling law between maximum displacement ( D ) and length ( L ), of the form D = cL n . This expression defines a model of fault growth by radial propagation from a single seed fracture or fault. This paper presents geometrical and kinematic evidence from a set of exceptionally well exposed normal faults in Utah for an alternative model of fault growth. This model is referred to as growth by segment linkage, and involves the propagation and linkage of independent fault segments on ascending length scales. The evidence presented focuses on the geometry and displacement variation in the region of relay structures, and on local scaling relationships between D and L . The D - L data from 97 faults in the study area range over three orders of magnitude, and show a general trend to increasing D for increasing L . There is a large scatter in the data, similar to that recognized in previous D - L compilations. It is argued that the scatter cannot be attributed either to measurement errors or to variation in mechanical properties. Instead, we argue that the model of growth by segment linkage provides a simple explanation of this scatter, and propose that the process of segment linkage may explain scatter in other datasets.

Empirical growth models are widely used to predict the growth and yield of plantation tree species, and the precise estimation of site quality is an important component of these models. The most commonly used proxy for site quality in growth models is Site Index (SI), which describes the mean height of dominant trees at a specified base age. Although SI is widely used, considerable research shows significant site-dependent variation in height for a given volume, with this latter variable more closely reflecting actual site productivity. Using a national dataset, this study develops and describes a stand-level growth and yield model for even-aged New Zealand-grown coast redwood (Sequoia sempervirens). We used a novel modelling approach that quantifies site quality using SI and a volume-based index termed the 300 Index, defined as the volume mean annual increment at age 30 years for a reference regime of 300 stems ha−1. The growth model includes a number of interrelated components. Mean top height is modelled from age and SI using a polymorphic Korf function. A modified anamorphic Korf function is used to describe tree quadratic mean diameter (Dq) as a function of age, stand density, SI and a diameter site index. As the Dq model includes stand density in its formulation, it can predict tree growth for different stand densities and thinning regimes. The mortality model is based on a simple attritional equation improved through incorporation of the Reineke stand density index to account for competition-induced mortality. Using these components, the model precisely estimates stand-level volume. The developed model will be of considerable value to growers for yield projection and regime evaluation. By more robustly describing the site effect, the growth model provides researchers with an improved framework for quantifying and understanding the causes of spatial and temporal variation in plantation productivity.

This study was carried to evaluate the efficiency of the Bitterlich method in growth and yield modeling of the even-aged Eucalyptus stands. 25 plots were setup in Eucalyptus grandis cropped under a high bole system in the Central Western Region of Minas Gerais, Brazil. The sampling points were setup in the center of each plot. The data of four annual mesurements were colleted and used to adjust the three model types using the age, the site index and the basal area as independent variables. The growths models were fitted for volume and mass of trees. The efficiency of the Bitterlich method was confirmed for generating the data for growth and yield modeling.

Forest growth and yield models are tools designed to provide forest managers with quantitative information on plantation development dynamics, the influence of various silvicultural manipulations like vegetation control, thinning, and fertilization, and the potential quantity and quality of forest products. Growth and yield models have a long history of development and use with increasing attention on modeling intensively managed plantations. Based on their construction and assumptions, growth models are of three primary types, namely (1) statistical; (2) mechanistic; and (3) hybrid. Both spatially-dependent and spatially-independent versions of these different model types have been used to model intensively management plantations. This chapter will explore these different modeling approaches, their ability to represent key silvicultural activities, and provide suggestions on the development and use of growth models for forest plantation management.

Abstract. Putra PCP, Wahyudi ST, Sambah AB, Sartimbul A. 2024. Growth and mortality model of Caesio cuning in Karimunjawa National Park, Indonesia. Biodiversitas 25: 4215-4222. Global climate change causes an increase in sea surface temperatures and changes in current patterns, which implies the occurrence of marine deoxidation, which has an impact on fish reproduction and growth and coupled with high levels of exploitation, which causes a decrease in fisheries productivity in the Karimunjawa National Park Area, one of which is yellowtail (Caesio cuning (Bloch, 1791)). This study aims to analyze yellowtail growth and mortality model with a biology approach. Samples were collected from as many as 900 individuals during the Northwest and Southeast monsoon season. Data were analyzed using the von Bertalanffy growth model with FAO-ICLARM Stock Assessment Tools II. The results show a significant relationship between the Northwest and Southeast monsoon seasons where growth is negative allometric because b&lt;3 with r2 values ??in each season of 0.85 and 0.89, the growth model obtained (Lt = 389.6[1 - e0.22-(t0+0.37)]) with total mortality of 2.02, where fishing mortality (F) 1.69 and natural mortality (M) 0.33 and produces an exploitation level (E) of 0.84 which indicates that yellowtail has been fully exploited as indicated by the value of E&gt;0.5. The highest recruitment pattern occurs in the Southeast monsoon season (July), amounting to 19.46%. This means that sustainable management is needed to maintain the stock of yellowtail resources in the Karimunjawa National Park Area.