Plant growth model

The potential contributions of plant calorimetry to understanding plant physiology that were postulated years ago by Pierce, Prat, and others were not realizable prior to development of an appropriate model relating plant metabolism, i.e. 0, Rcoi and / o values to growth and substrate carbon conversion efficiency [20], 

Initial insight into necessary components of a growth model that includes both energy and mass, came from recognition that dark respiration rate has 

If one or more of the inputs is present in less than optimum amount, then the respiratory rate and efficiency, and hence the formation of structural biomass (growth rate), will be reduced to the rate determined by the rate of acquisition of 

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This plantation model is an extension and generalization of work done in previous studies (40.41). The model includes a number of submodels which are discussed below. These submodels include (1) land resources. (2) data base on plants. (3) recycled inputs. (4) species selection and characterization. (5) plant growth model. (6) data base for field operations. (7) field operations. (8) data base for cost estimates, and (9) cost estimate for biomass produced. 

Global climate change is predicted to have major consequences for plant growth and this will provide a fresh impetus for the mathematical modeling of the rhizosphere (101). 

The way in which models estimate PET and AET values, plant growth, root growth and distribution, and other parameters can have profound effects on the accuracy of model estimates for ET landfill cover design. For example14 ... 

Development of the Environmental Policy Integrated Climate (EPIC) model and its predecessor, the Erosion Productivity Impact Calculator, began in the early 1980s.69 70 The first version of EPIC was intended to evaluate the effects of wind and water erosion on plant growth and food production. More recent versions also evaluate factors important to other environmental issues. EPIC is a onedimensional model however, it can estimate lateral flow in soil layers at depth. All versions of EPIC estimate surface runoff, PET, AET, soil-water storage, and PRK below the root zone—these complete the hydrologic water balance for an ET landfill cover. 

UNSAT-H does not address the effects of soil density on plant growth and water balance. Disadvantages caused by the computational methods used to estimate soil water flow include the following (1) the model requires the user to choose from several submodels to solve the Richards equation this choice should be made by a person with training in advanced soil physics and (2) the model requires the input of several soil parameters that are difficult to estimate for the completed cover soil. 

Both of them require at least limited model calibration. They do not stochastically estimate daily climate data for model evaluations or long-term changes in plant nutrient status and the resulting changes in plant growth and water balance. HYDRUS and UNSAT-H would be very useful and accurate if used in research however, they are difficult to use in engineering design of ET landfill covers and provide incomplete estimates of performance. 

Soil parameter inputs, their use within the model, and appropriateness of estimates that affect plant growth, and water use and storage. 

Possible latent drawbacks of the SFI model were pointed out by Lu et al. (2002). SFI may largely depend on pH, but an extremely high pH value is not suitable for plant growth. Moreover, pH is not an independent variable, but dependent on relative proportions of Ca, Mg and exchangeable Al in soil. Thus, they developed another index called an SEF that was calculated by the following equation. 

The Rothamsted Carbon Model (RothC) uses a five pool structure, decomposable plant material (DPM), resistant plant materials (RPM), microbial biomass, humified organic matter, and inert organic matter to assess carbon turnover (Coleman and Jenkinson 1996 Guo et al. 2007). The first four pools decompose by first-order kinetics. The decay rate constants are modified by temperature, soil moisture, and indirectly by clay content. RothC does not include a plant growth sub-module, and therefore NHC inputs must be known, estimated, or calculated by inverse modeling. Skjemstad et al. (2004) tested an approach for populating the different pools based on measured values. 

Next, the studies were extended to evaluate the effect of the compounds on duckweed which is one of the best characterized models for assessing phytotoxic activity. The duckweed assay system makes it possible to study the toxic effects throughout the plant life cycle, as well as to plant specific toxic effects which target photosynthesis. Of all the isolates, only 12 and 13 showed significant phytotoxicity on duckweed at concentrations of 100 xM and 200 xM inhibiting plant growth by 80% and 100%, respectively and chlorophyll production by 40% and 84%, respectively. ... 

A number of issues influenced the selection of the dose-response model form and the treatment of the data prior to fitting the model. First, shoot weight and shoot length are continuous response measurements therefore, use of a standardized logistic model form is not appropriate. Second, the natural variation in plant growth often resulted in apparent increased shoot weight and shoot length measurements relative to the control at low herbicide application rates. A dose-response model needs to perform well even when some measurements in treatment levels exceed the controls. 

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