Multivariate global sensitivity analysis for dynamic crop models

Abstract

Dynamic crop models are frequently used in ecology, agronomy and environmental sciences for simulating crop and environmental variables at a discrete time step. They often include a large number of parameters whose values are uncertain, and it is often impossible to estimate all these parameters accurately. A common practice consists in selecting a subset of parameters by global sensitivity analysis, estimating the selected parameters from data, and setting the others to some nominal values. For a discrete-time model, global sensitivity analyses can be applied sequentially at each simulation date. In the case of dynamic crop models, simulations are usually computed at a daily time step and the sequential implementation of global sensitivity analysis at each simulation date can result in several hundreds of sensitivity indices, with one index per parameter per simulation date. It is not easy to identify the most important parameters based on such a large number of values. In this paper, an alternative method called multivariate global sensitivity analysis was investigated. More precisely, the purposes of this paper are (i) to compare the sensitivity indices and associated parameter rankings computed by the sequential and the multivariate global sensitivity analyses, (ii) to assess the value of multivariate sensitivity analysis for selecting the model parameters to estimate from data. Sequential and multivariate sensitivity analyses were compared by using two dynamic models: a model simulating wheat biomass named WWDM and a model simulating N2O gaseous emission in crop fields named CERES-EGC. N2O measurements collected in several experimental plots were used to evaluate how parameter selection based on multivariate sensitivity analysis can improve the CERES-EGC predictions. The results showed that sequential and multivariate sensitivity analyses provide modellers with different types of information for models which exhibit a high variability of sensitivity index values over time. Conversely, when the parameter influence is quite constant over time, the two methods give more similar results. The results also showed that the estimation of the parameters with the highest sensitivity indices led to a strong reduction of the prediction errors of the model CERES-EGC

Sensitivity Analysis of the APEX Model for Assessing Sustainability of Switchgrass Grown for Biofuel Production in Central Texas

“…To date, no formal sensitivity analyses have been performed on perennials grown for biofuel and most sensitivity analyses performed for annual crops have mainly focused on aboveground growth parameters [35,52]. This study investigates 45 parameters relevant to growth and nutrients cycling of switchgrass to analyze the impact from both above and belowground parameters for perennial grass.…”

“…However, caution should be taken when simplifying the complex structure of crop models because some model parameters may have little effect on certain response variables, but they may play key roles in other processes [65]. Additionally, the dynamic characteristics of parameter sensitivity need to be considered because parameter sensitivity may evolve over time [35]. The ":" denotes an interaction between two parameters a The definitions and the ranges of these identified parameters are listed in Table 1 b The response range is (the minimum value, the maximum value) under the interaction for each response variable c The percentage change was calculated as the difference between the maximum and the minimum value divided by the minimum d Average is the averaged percentage change across all the interactions for CROP and PARM, respectively…”

“…Sensitivity analysis identifies the governing parameters in complex simulation models by calculating how much a model output changes due to a minor change in its input parameter value [35,47,52]. For this study, a quantitative measure of sensitivity was conducted using the enhanced version of Morris method, the measure of which is the mean of the absolute values of Morris local measures and is believed to be good for ranking parameters in order of importance [8].…”

A meta-modeling approach for spatio-temporal uncertainty and sensitivity analysis: an application for a cellular automata-based Urban growth and land-use change model

“…Since PCE reduces the computational expense of uncertainty propagation, it has been widely applied in complex environmental problems including water quality modeling (Moreau et al 2013), large scale socio-hydrologic modeling coupled with Agent-Based Models (Hu et al 2015), groundwater hydrogeological modeling (Deman et al 2016) and in other dynamic modeling examples such as crop modeling (Lamboni et al 2009) and seawater intrusion (Rajabi et al 2015). Moreover, an extensive review of basic principles and applications of PCE in computational fluid dynamics was conducted by Najm (2009).…”

Uncertainties in assessing the effect of climate change on agriculture using model simulation and uncertainty processing methods

“…Considering high parameter correlation in models, recently developed multivariate and global sensitivity analyses allow the simultaneous testing of multiple parameter changes and can analyze direct and indirect effects of each parameter, the advantages of which are well illustrated in the sensitivity analysis of crop model parameters [67,68]. Considering that previous sensitivity analyses were only concerned with the effect on the final output results, it has been recently suggested that it is necessary to perform a multivariate time series of the sensitivity analysis for the daily output of the results obtained from a discontinuous-step simulation model [69], and it is important to reduce the uncertainty in the model parameter estimation. It is noted that, if we consider the model structure of uncertainty based on a single model of sensitivity analysis, possible errors in the model structure of uncertainty will propagate to the assessment of parameter uncertainty [70].…”