Modeling gross primary production of maize and soybean croplands using light quality, temperature, water stress, and phenology

Vegetation productivity metrics, such as gross primary production (GPP) may be determined from the efficiency with which light is converted into photosynthates, or light use efficiency (ε). Therefore, accurate measurements and modeling of ε is important for estimating GPP in each ecosystem. Previous studies have quantified the impacts of biophysical parameters on light use efficiency based GPP models. Here we enhance previous models utilizing four scalars for light quality (i.e., cloudiness), temperature, water stress, and phenology for data collected from both maize and soybean crops at three Nebraska AmeriFlux sites between 2001 and 2012 (maize: 26 field-years; soybean: 10 field-years). The cloudiness scalar was based on the ratio of incident photosynthetically active radiation (PARin) to potential (i.e., clear sky) PARpot. The water stress and phenology scalars were based on vapor pressure deficit and green leaf area index, respectively. Our analysis determined that each parameter significantly improved the estimation of GPP (AIC range: 2503–2740; likelihood ratio test: p-value < 0.0003, df = 5–8). Daily GPP data from 2001 to 2008 calibrated the coefficients for the model with reasonable amount of error and bias (RMSE = 2.2 gCm−2d−1; MNB= 4.7%). Daily GPP data from 2009 to 2012 tested the model with similar accuracy (RMSE = 2.6 gCm−2d−1; MNB= 1.7%). Modeled GPP was generally within 10% of measured growing season totals in each year from 2009 to 2012. Cumulatively, over the same four years, the sum of error and the sum of absolute error between the measured and modeled GPP, which provide measures of long-term bias, was ±5% and 2–9%, respectively, among the three sites.