Comparison of several models for estimating gross primary production of drip-irrigated maize in arid regions

Abstract

Gross primary productivity (GPP) is the total amount of carbon absorbed by green plants through photosynthesis. As an important part of the global carbon cycle, photosynthesis is the process of converting solar energy into chemical energy. Therefore, accurate estimation of GPP is important for understanding the regional and global carbon flux variations. Based on the flux data of spring maize under drip irrigation in northwest China during 2014∼2018, this study compared and evaluated the simulation accuracy of several models for estimating GPP under different growing seasons and environmental conditions, and optimized the rectangular hyperbolic (RH) model. The results showed that none of these models consistently had the highest simulation accuracy over a 5-year period. The total GPP simulated by the light response photosynthetic models was closer to the observed value than the light use efficiency (LUE) model and stomatal conductance models. Under different growing stages and environmental conditions, the simulation accuracy of different models was different: (1) RH model had the best simulation effect when the maize was completely covered (i.e. LAI>2), but stomatal conductance-photosynthesis-transpiration coupling (SMPT-SB) model had the best simulation effect in the early growing stage; (2) when the soil water content was high, RH model has the best simulation effect. On the contrary, the simulation effect of the non-rectangular hyperbolic (N-RH) model was slightly better than other models; (3) the LUE model has a strong dependence on light conditions. Therefore, when Rn≤100, all the other 8 models except the LUE model could well simulate GPP. When Rn>100, the RH model had the best simulation effect; (4) when VPD≤1.5, the simulation accuracy of RH model was the highest; when VPD>1.5, SMPT-SB had the best simulation effect. To obtain parameters in the models more quickly and accurately, we improved the RH model by establishing the relationship between maximum photosynthetic rate (Pmax) and leaf area index (LAI), initial quantum efficiency (α) and LAI, and obtained the RH-LAI model. Compared with the RH model, the simulation accuracy of the improved RH-LAI model was improved. And the reliability of the RH-LAI model was verified in other sites. Therefore, the research results showed that in the regions with fewer observation indexes, we can use the RH-LAI model with a simple structure and convenient calculation to calculate the daily GPP by integrating it on the time scale.