Soil quality model

Without a solution, formulated mathematical systems (models) are of little value. Four solution procedures are mainly followed the analytical, the numerical (e.g., finite different, finite element), the statistical, and the iterative. Numerical techniques have been standard practice in soil quality modeling. Analytical techniques are usually employed for simplified and idealized situations. Statistical techniques have academic respect, and iterative solutions are developed for specialized cases. Both the simulation and the analytic models can employ numerical solution procedures for their equations. Although the above terminology is not standard in the literature, it has been used here as a means of outlining some of the concepts of modeling. 

Generally speaking, a deterministic or stochastic soil quality model consists of two major parts of modules ... 

The above two modules form the soil quality model. The flow module drives the solute module. It is important to note that the moisture module can be absent from the model and in this case a model user has to input to the solute module information that would have been either produced by a moisture module, or would have been obtained from observed data at a site. 

Model selection, application and validation are issues of major concern in mathematical soil and groundwater quality modeling. For the model selection, issues of importance are the features (physics, chemistry) of the model its temporal (steady state, dynamic) and spatial (e.g., compartmental approach resolution) the model input data requirements the mathematical techniques employed (finite difference, analytic) monitoring data availability and cost (professional time, computer time). For the model application, issues of importance are the availability of realistic input data (e.g., field hydraulic conductivity, adsorption coefficient) and the existence of monitoring data to verify model predictions. Some of these issues are briefly discussed below. 

Numerical soil models (time, space) provide a general tool for quantitative and qualitative analyses of soil quality, but require time consuming applications that may result in high study costs. In addition input data have to be given for each node or element of the model, which model has to be run twice, the number of rainfall events. On the other hand, analytic models obtained from analytic solutions of equation (3) are easier to use, but can simulate only averaged temporal and spatial conditions, which may not always reflect real world situations. Statistical models may provide a compromise between the above two situations. 

P cycling, pre chain emissions, animal welfare, economics, biodiversity, product quality, soil quality, and landscape aesthetics [60]. Whole farm model (WFM) uses pasture growth and cow metabolism for predicting CH4 emissions in dairy farms. Also included in the WFM is climate and management information. However, recent reports also suggests that WFMs may incorrectly estimate CH4 emission levels as they do not take into account the DMI and diet composition while predicting the enteric CH4 emission. This low prediction efficiency of WFMs may lead to substantial error in GHG inventories [10,11],... 

But science is neither value free nor independent. Values do and should enter into important phases of the research process such as problem identification, design of methods and experiments, model assumptions and the use of normative concepts (Alroe and Kristensen 2002). Some concepts that are widely used in agricultural research are clearly value laden. Obvious examples include sustainability, food quality, soil quality, nature quality, animal welfare, rural development and human wellbeing. Such concepts often have different meanings in different groups, discourses and research disciplines. These conceptual differences influence the kinds of... 

M. 1. Kaniu, K. H. Angeyo, A. K. Mwala and F. K. Mwangi, Energy dispersive X-ray fluorescence and scattering assessment of soil quality via partial least squares and artificial neural networks analytical modeling approaches, Talanta, 2012, 98, 236-240. 

Of the 41 listed in Table 4.1 the 16 most common mass transport processes representing the air, water, and soil and sediment media appear in Table 4.2. The media of prime concern often dictate the most convenient phase concentration used in the flux equation. For example, water quality models usually have Cw as the state variable and therefore the flux expression must have the appropriate MTC group based on Cw and these appear in the center column of Table 4.2. Aquatic bed sediment models usually have Cs, the chemical loading on the bed solids, as the state variable. The MTC groups in the right eolumn are used. All the MTC groups in Table 4.2 contain a basic transport parameter that reflects molecule, element, or particle mobility. Both diffusive and advective types appear in the table. These are termed the individual phase MTCs with SI units of m/s. Examples of each type in Table 4.2 include for water solute transport and Vg for sediment particle deposition (i.e., setting). 

One weakness of some multimedia models that must be considered by the user is inconsistency of time scales. For example, if we employ monthly averaged air concentrations to get rainout values on fifteen-minute interval inputs to a watershed model, large errors can obviously occur. The air-land-water-simulation (ALWAS) developed by Tucker and co-workers (12) overcomes this limitation by allowing for sequential air quality outputs to provide deposition data to drive a soil model. This in turn is coupled to a surface water model. 

Alemi, M. H., D. A. Goldhamer and D. R. Nelson, 1991, Modeling selenium transport in steady-state, unsaturated soil columns. Journal of Environmental Quality, 20,89-95. 

The quality of the final product is partly determined by the hygiene/cleaning systems in the factory. So aspects such as modeling of microbiological growth, decontamination, soil removal and the hygiene level in the process become essential. 

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