Sensitivity and Uncertainty Analyses of Crop Yields and Soil Organic Carbon Simulated With Epic

Abstract

Modeling biophysical processes is a complex endeavor because of large data requirements and uncertainty in model parameters. Model predictions should incorporate, when possible, analyses of their uncertainty and sensitivity. The study incorporated uncertainty analysis on EPIC (Environmental Policy Impact Calculator) predictions of corn (Zea mays L.) yield and soil organic carbon (SOC) using generalized likelihood uncertainty estimation (GLUE). An automatic parameter optimization procedure was developed at the conclusion of sensitivity analysis, which was conducted using the extended Fourier amplitude sensitivity test (FAST). The analyses were based on an experimental field under 34-year continuous corn with five N treatments at the Arlington Agricultural Research Station in Wisconsin. The observed average annual yields per treatment during 1958 to 1991 fell well within the 90% confidence interval (CI) of the annually averaged predictions. The width of the 90% CI bands of predicted average yields ranged from 0.31 to 1.6 Mg ha −1. The predicted means per treatment over simulations were 3.26 to 6.37 Mg ha −1 , with observations from 3.28 to 6.4 Mg ha −1. The predicted means of yearly yield over simulations were 1.77 to 9.22 Mg ha −1 , with observations from 1.35 to 10.22 Mg ha −1. The 90% confidence width for predicted yearly SOC in the top 0.2 m soil was 285 to 625 g C m −2 , while predicted means were 5122 to 6564 g C m −2 and observations were 5645 to 6733 g C m −2. The optimal parameter set identified through the automatic parameter optimization procedure gave an R 2 of 0.96 for average corn yield predictions and 0.89 for yearly SOC. EPIC was dependable, from a statistical point of view, in predicting average yield and SOC dynamics.

Lessons from climate modeling on the design and use of ensembles for crop modeling

“…A simpler alternative that has been applied to crop models is the GLUE algorithm, which explores the space of possible parameter values, calculates a likelihood and eliminates parameter vectors whose likelihood is below a threshold (Wang et al 2005). Controversy concerning GLUE due to its subjective aspects is summarized in (Beven and Binley 2014).…”

An EPIC model-based vulnerability assessment of wheat subject to drought

“…The simulations through SCE-UA are regarded as valuable as they sample the entire parameter space, with a focus on solutions near the optimum (Xu et al 2013). Based on previous studies (Wang et al 2005;Wu et al 2009;Liu 2009), the following six parameters were selected for calibration: (1) potential radiation use efficiency (WA), (2) harvest index (HI), (3) point in the growing season when leaf area begins to decline due to leaf senescence (DLAI), (4) normal fraction of N in crop biomass at mid-season (BN2), (5) potential heat unit (PHU), and (6) crop parameter control leaf area growth of the crop under non-stressed condition (DLP2), which are the most sensitive parameters for crop yield in the EPIC model. The calculation procedure and parameter settings of the algorithm are based on Duan et al (1993Duan et al ( , 1994.…”

“…3). In addition to agricultural management factors, factors such as the sowing and harvesting date (Wu et al 2009), potential radiation use efficiency (WA), harvest index (HI), the point in the growing season at which leaf area begins to decline due to leaf senescence (DLAI), the normal fraction of N in crop biomass at mid-season (BN2), potential heat unit (PHU), and crop parameter control of leaf area growth in a crop under a non-stressed condition (DLP2) (Wang et al 2005;Wu et al 2009;Liu 2009), have significant impacts on wheat yield, whether in reality or in model processing, due to the complexity of the wheat production system. It is not only time-consuming, but also very difficult to obtain satisfactory accuracy by calibrating and validating the EPIC model while adjusting parameters manually Yin et al 2014).…”

“…However, this method, which uses the vulnerability curves of only two wheat varieties to assess vulnerability in China, is insufficient and requires a high degree of simplification, as it does not take territorial differentiation into account despite its large scale. Crop varieties, which are crucial parameters and have significant influence on wheat yield (Wang et al 2005;Wu et al 2009), have for the most part been ignored in previous studies. In addition, the crop calendar, which is obtained from statistical data, has a temporal resolution of 10 days, which is not precise enough and does not fit the model well as a crucial and sensitive parameter.…”

Monte Carlo-based calibration and uncertainty analysis of a coupled plant growth and hydrological model

“…In a number of studies (e.g. Pathak et al, 2012;Wang et al, 2005;Iizumi et al, 2009) the hydrological model has been calibrated as a first step and the plant growth model as a second step in order to reduce the number of parameters varied in one calibration step. However, in such a setup, feedbacks between biomass production and hydrology are not considered (Pauwels et al, 2007).…”