Assessment of the adequacy of mathematical models

By L. Tedeschi. 2006

732 citations recorded by the source corpus.

Subfields: Environmental Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/5ecd4f969c4d9ae562eb0451e5263244bb001810

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In Environmental Engineering

In Management Science and Operations Research

Citations in the corpus

Description and evaluation of the Farmax Dairy Pro decision support model (2010, background): “Further details of these statistics are available in Tedeschi ( 2006 ).”
Evaluation and application of the CPM Dairy Nutrition model (2007, methodology): “The distributions were similar but more points might be needed to obtain a normal distribution for the observed dataset and secure an adequate level for the Type II error – the error of accepting model adequacy when, in fact, models differ (Tedeschi 2006).”
A nutrition mathematical model to account for dietary supply and requirements of energy and other nutrients for domesticated small ruminants: The development and evaluation of the Small Ruminant Nutrition System (2008, methodology): “This finding supports the need to adjust for changes in BCS or BW to accurately predict ME or MP available for lactation as discussed by TEDESCHI et al. (2006).”
Simulating pasture growth rates in Australian and New Zealand grazing systems (2008, unknown): “Third, a range of model evaluation statistics were calculated, based on the work of Tedeschi (2006), to compare the mean measured and predicted monthly net herbage accumulation rates andseasonalyields.”
Manual for building tree volume and biomass allometric equations: from field measurement to prediction (2012, unknown): “1 Validation criteria Several criteria, which are the counterparts of the criteria used to evaluate the quality of model fitting, may be used to compare predictions with observations (Schlaegel, 1982; Parresol, 1999; Tedeschi, 2006), in particular: • bias: ∑n′ i=1 |Y ′ i − Ŷ ′ i | • sum of squares o”
Cornell Net Carbohydrate and Protein System: A model for precision feeding of dairy cattle (2008, methodology): “…values, the simultaneous probability to test for intercept = 0 and slope = 1, the linear regression mean square error (MSE), the mean square error of prediction (MSEP), the concordance correlation coefficient (CCC), and the MSEP decomposition for all evaluations as described by Tedeschi (2006).”
System dynamics modeling for agricultural and natural resource management issues: Review of some past cases and forecasting future roles (2016, methodology): “, Monte Carlo simulation) and model boundary and extreme condition testing to gauge the model’s respect of physical and economic laws; and (3) analysis of scenarios compared to expert opinions (for model calibration and evaluation techniques, see [33,131,132]).”
Evaluation of enteric methane prediction equations for dairy cows used in whole farm models (2010, background): “This is in line with Tedeschi (2006) who discussed and compared several techniques to evaluate mathematical models designed for predictive purposes, and concluded that such an evaluation is only possible through the use of a combination of several statistical analyses.”
The mathematical description of lactation curves in dairy cattle (2011, background): “However, it is clear that a function suitable for all the fields of applications hardly exists and that model validity should be assessed on the basis of its sustainability for a particular purpose (Tedeschi, 2006).”