Transferability of jarvis-type models developed and re-parameterized for maize to estimate stomatal resistance of soybean: analyses on model calibration, validation, performance, sensitivity, and elasticity

Authors

Denis Mutiibwa, University of Nevada, Reno, Nevada
Suat Irmak, University of Nebraska-LincolnFollow

Date of this Version

2013

Citation

Transactions of the ASABE Vol. 56(2): 409-422

Comments

© 2013 American Society of Agricultural and Biological Engineers

Abstract

In a previous study by the same authors, a new modified Jarvis model (NMJ-model) was developed, calibrated, and validated to estimate stomatal resistance (rs) for maize canopy on an hourly time step. The NMJ-model’s unique subfunctions, different from the original Jarvis model (J-model), include a photosynthetic photon flux density

(PPFD)-r_s response subfunction developed from field measurements and a new physical term, Aexp(1/LAI), where A is the minimum stomatal resistance and LAI is the green leaf area index, to account for the influence of canopy development on r_s, especially during partial canopy stage in the early season and in late-season stage during leaf aging and senescence. This study evaluated the transferability of the J-model and NMJ-models that were re-parameterized and calibrated for maize canopy to estimate soybean r_s. Due to the differences in physiological and photosynthetic pathway differences between the two crops, the rs response to the same environmental variables, i.e., PPFD, vapor pressure deficit (VPD), and air temperature (T_a), were substantially different. Thus, this study demonstrated the inherent limitation in applying the Jarvis-type models that were calibrated for maize to soybean without re-calibration. Maize-calibrated models performed poorly in estimating soybean rs, with the coefficient of determination (r²) ranging from 0.30 to 0.38 and the root mean square difference (RMSD) between the estimated and measured rs ranging from 94.4 to 166 s m^-1. The J-model and NMJ model were re-calibrated by parameter optimization method for soybean. The J-model calibrated well; however, the validation had poor performance results. The NMJ-model had a good calibration, resulting in a good r² (0.71) and a small

RMSD (13.7 s m^-1). The NMJ-model validation produced superior results to the J-model, explaining more than 80% of the variation in the measured rs (RMSD = 38.4 s m^-1). These results show the robustness and practical accuracy of the NMJ model in estimating r_s over different canopies if well calibrated for a specific crop. In terms of sensitivity and elasticity analyses, among all parameters, r_s estimates were most sensitive to uncertainties introduced in parameter a₁ of the PPFD subfunction due to its exponential impact on r_s in the NMJ-model. Therefore, for accurate estimates of r_s, uncertainties in parameter a₁ should not exceed the range of -2% and 2% so that the error in estimated r_s is kept between -3.5% and 3.6%. The study observed that the relative change in rs due to uncertainties in parameters a₂ and a₃ of the VPD subfunction was a linear function and less sensitive than the PPFD subfunction. The sensitivity of rs to uncertainties in temperature subfunction parameters (a₄ and a₅) was higher than that of VPD subfunction parameters, but less than that of PPFD subfunction parameters. The uncertainty in parameters a₄ and a₅ should range within -10% and 10%, and the calibration of these parameters should be determined with greater precision as compared with the VPD subfunction parameters. The study confirmed that the addition of the r_{s_min} and the A^exp(1/LAI) terms, which were not accounted for in the original J-model, improved the model accuracy for estimating soybean r_s.