On the ratio of intercellular to ambient CO2 (c i/c a) derived from ecosystem flux

Emerging contaminants in water used for maize irrigation: Economic and food safety losses associated with ciprofloxacin and glyphosate

Chemicals used to assure agricultural production and the feasibility of planting sites often end up in bodies of water used for crop irrigation. In a pot study, we investigated the consequences associated with the irrigation of maize with water contaminated by ciprofloxacin (Cipro; 0, 0.2, 0.8, 1.4 and 2.0 μg l-1) and/or glyphosate (0, 5, 25 and 50 mg l-1) on yields and food safety. Glyphosate in concentrations ≥25 mg l-1 prevented plant establishment, regardless of Cipro presence. Evaluations made at the V5 stage of plants reveal that Cipro concentrations ≥0.8 μg l-1 and glyphosate decreased photosynthesis and induced changes in leaf anatomy and stem biophysical properties that may contribute to decreased kernel yields. When those chemicals were applied together, kernel yield reductions were accentuated, evidencing their interactive effects. Irrigation with contaminated water resulted in accumulations of Cipro and glyphosate (as well as its metabolite, aminomethylphosphonic acid) in plant tissues. Accumulation of these chemicals in plant tissues such as leaves and kernels is a problem, since they are used to feed animals and humans. Moreover, these chemicals are of potential toxicological concern, principally due to residue accumulations in the food chain. Specially, the antibiotic residue accumulations in maize tissues can assist the induction of antibiotic resistance in dangerous bacteria. Therefore, we point out the urgency of monitoring the quality of water used for crop irrigation to avoid economic and food-quality losses.

Sensitivity and Uncertainty Analyses of Flux-based Ecosystem Model towards Improvement of Forest GPP Simulation

An ecosystem model serves as an important tool to understand the carbon cycle in the forest ecosystem. However, the sensitivities of parameters and uncertainties of the model outputs are not clearly understood. Parameter sensitivity analysis (SA) and uncertainty analysis (UA) play a crucial role in the improvement of forest gross primary productivity GPP simulation. This study presents a global SA based on an extended Fourier amplitude sensitivity test (EFAST) method to quantify the sensitivities of 16 parameters in the Flux-based ecosystem model (FBEM). To systematically evaluate the parameters’ sensitivities, various parameter ranges, different model outputs, temporal variations of parameters sensitivity index (SI) were comprehensively explored via three experiments. Based on the numerical experiments of SA, the UA experiments were designed and performed for parameter estimation based on a Markov chain Monte Carlo (MCMC) method. The ratio of internal CO2 to air CO2 ( f C i ) , canopy quantum efficiency of photon conversion ( α q ) , maximum carboxylation rate at 25 ° C ( V m 25 ) were the most sensitive parameters for the GPP. It was also indicated that α q , E V m and Q 10 were influenced by temperature throughout the entire growth stage. The result of parameter estimation of only using four sensitive parameters (RMSE = 1.657) is very close to that using all the parameters (RMSE = 1.496). The results of SA suggest that sensitive parameters, such as f c i , α q , E V m , V m 25 strongly influence on the forest GPP simulation, and the temporal characteristics of the parameters’ SI on GPP and NEE were changed in different growth. The sensitive parameters were a major source of uncertainty and parameter estimation based on the parameter SA could lead to desirable results without introducing too great uncertainties.