An agro-ecological simulation model system

In this paper five different models, as five modules of a complex agro-ecosystem are investigated. The water and nutrient flow in soil is simulated by the nutrient-in-soil model while the biomass change according to the seasonal weather aspects, the nutrient content of soil and the biotic interactions amongst the other terms of the food web are simulated by the food web population dynamical model that is constructed for a piece of homogeneous field. The food web model is based on the nutrient-in-soil model and on the activity function evaluator model that expresses the effect of temperature. The numbers of individuals in all phenological phases of the different populations are given by the phenology model. The food web model is extended to an inhomogeneous piece of field by the spatial extension model. Finally, as an additional module, an application of the above models for multivariate state-planes, is given. The modules built into the system are closely connected to each other as they utilize each other’s outputs, nevertheless, they work separately, too. Some case studies are analysed and a summarized outlook is given.

The calculation of dynamic ecological footprint on the basis of the dynamic input-output model

The Leontief input-output model is widely used to determine the ecological footprint of consumption in a region or a country. It is able to capture spillover environmental effects along the supply change, thus its popularity is increasing in ecology related economic research. These studies are static and the dynamic investigations are neglected. The dynamic Leontief model makes it possible to involve the capital and inventory investment in the footprint calculation that projects future growth of GDP and environmental impacts. We show a new calculation method to determine the effect of capital accumulation on ecological footprint. Keywords: Dynamic Leontief model, Dynamic ecological footprint, Environmental management, Allocation method

Hajózni muszáj! - A GDP, az ökológiai lábnyom és a szubjektív jóllét stratégiai összefüggései ("Navigare necesse est." The connections between GDP, ecological footprint, and subjective welfare)

A tanulmányban országok környezetterhelését és jóllétét vizsgáljuk. A környezetterhelést vizsgáló klasszikus I = PAT azonosság célszerű átrendezésével és továbbgondolásával két térképet szerkesztünk, amelyek egy véges, korlátozott erőforrású világban segítik a stratégiai döntéshozatalt. A térképek adatait a bruttó hazai termék, az ökológiai lábnyom, illetve a szubjektív jóllét mutatói szolgáltatják, ezek az információk a világ országaira ma már széles körben rendelkezésre állnak. Bemutatjuk a gazdasági tevékenység és a szubjektív jóllét kapcsolatát, s az erre épülő 12 stratégiát, majd a szűkös természeti erőforrásokkal számot vető újabb 12 stratégialehetőséget tárgyaljuk. A gazdasági tevékenységet, a környezeti korlátokat és az emberi boldogságot egyszerre szem előtt tartó modell alapján nyilvánvaló a makroszintű következtetés: napjainkban a gazdasági tevékenység, illetve az emberi jóllét dematerializációja szükséges és kívánatos célkitűzés /===/ The study examines countries' ecological footprint and welfare. Rearranging and developing the classic I = PAT equation, the author has devised two maps to assist strategic decision-making in a world of finite, limited resources. The data on the maps – GDP, ecological footprint, and subjective welfare indices – are all widely available all over the world. The relation between economic activity and subjective welfare is presented and twelve strategies built upon them, before discussing twelve further strategies based on scarce natural resources. Using a model that considers economic activity, environmental constraints and human happiness concurrently, it becomes obvious that dematerialization of economic activity and human welfare are a necessary and desirable objective.

Simulations of Theoretical Ecosystem Growth Model (TEGM) during various climate conditions

Climate change has a great impact on the build and the work of natural ecosystems. Disappearance of some population or growth of the number in some species can be already caused by little change in temperature. A Theoretical Ecosystem Growth Model was investigated in order to examine the effects of various climate patterns on the ecological equilibrium. The answers of the ecosystems which are given to the climate change could be described by means of global climate modelling and dynamic vegetation models. The examination of the operation of the ecosystems is only possible in huge centres on supercomputers because of the number and the complexity of the calculation. The number of the calculation could be decreased to the level of a PC by considering the temperature and the reproduction during the modelling of a theoretical ecosystem and several important theoretical questions could be answered.

Simulation modeling of phytoplankton dynamics in a large eutrophic river, Hungary – Danubian Phytoplankton Growth Model (DPGM)

Ecological models have often been used in order to answer questions that are in the limelight of recent researches such as the possible effects of climate change. The methodology of tactical models is a very useful tool comparison to those complex models requiring relatively large set of input parameters. In this study, a theoretical strategic model (TEGM ) was adapted to the field data on the basis of a 24-year long monitoring database of phytoplankton in the Danube River at the station of G¨od, Hungary (at 1669 river kilometer – hereafter referred to as “rkm”). The Danubian Phytoplankton Growth Model (DPGM) is able to describe the seasonal dynamics of phytoplankton biomass (mg L−1) based on daily temperature, but takes the availability of light into consideration as well. In order to improve fitting, the 24-year long database was split in two parts in accordance with environmental sustainability. The period of 1979–1990 has a higher level of nutrient excess compared with that of the 1991–2002. The authors assume that, in the above-mentioned periods, phytoplankton responded to temperature in two different ways, thus two submodels were developed, DPGM-sA and DPGMsB. Observed and simulated data correlated quite well. Findings suggest that linear temperature rise brings drastic change to phytoplankton only in case of high nutrient load and it is mostly realized through the increase of yearly total biomass.