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Model uncertainty is rarely considered in the field of biogeochemical modeling. The standard biogeochemical modeling approach is to proceed based on one selected model with the “right” complexity level based on data availability. However, other plausible models can result in dissimilar answer to the scientific question in hand using the same set of data. Relying on a single model can lead to underestimation of uncertainty associated with the results and therefore lead to unreliable conclusions. Multi-model ensemble strategy is a means to exploit the diversity of skillful predictions from different models with multiple levels of complexity. The aim of this paper is two fold, first to explore the impact of a model’s complexity level on the accuracy of the end results and second to introduce a probabilistic multi-model strategy in the context of a process-based biogeochemical model. We developed three different versions of a biogeochemical model, TOUGHREACT-N, with various complexity levels. Each one of these models was calibrated against the observed data from a tomato field in Western Sacramento County, California, and considered two different weighting sets on the objective function. This way we created a set of six ensemble members. The Bayesian Model Averaging (BMA) approach was then used to combine these ensemble members by the likelihood that an individual model is correct given the observations. Our results demonstrated that none of the models regardless of their complexity level under both weighting schemes were capable of representing all the different processes within our study field. Later we found that it is also valuable to explore BMA to assess the structural inadequacy inherent in each model. The performance of BMA expected prediction is generally superior to the individual models included in the ensemble especially when it comes to predicting gas emissions. The BMA assessed 95% uncertainty bounds bracket 90–100% of the observations. The results clearly indicate the need to consider a multi-model ensemble strategy over a single model selection in biogeochemical modeling study.

Anthropogenic greenhouse gas (GHG) emissions from croplands are primarily attributed to nitrogen (N) fertilization in agricultural production. However, the interactive effects of various agricultural management practices, climatic conditions, soil properties, and fertilization on non-CO2 GHG emissions (specifically methane (CH4) and nitrous oxide (N2O)) and gross global warming potential (GGWP) have been scarcely discussed. In this study, we conducted a meta-analysis of 326 agricultural treatments in China from 76 literature sources to elucidate the relationship between the response ratio (RR) of GGWP (GGWP RR), CH4 (CH4 RR), and N2O emissions (N2O RR) and various explanatory variables using redundancy analysis. Generally, nitrogen fertilizer application increased the N2O and CH4 emissions and GGWP by 120.0%, 32.5%, and 107.9%, respectively. We found that the GGWP RR was closely related to the rate of organic fertilizer application and initial bulk density, while it showed a negative association with the initial total soil nitrogen content. We found that CH4-RR was positively associated with the rate of synthetic fertilizer application, and N2O-RR exhibited a positive association with initial soil organic carbon and annual mean precipitation. Notably, the total fertilizer application rate had the most significant impact on both the GGWP RR and the N2O RR, while mean annual precipitation contributed the most to CH4-RR. Furthermore, a sensitivity analysis using a machine learning model suggested that the GGWP RR was more sensitive to synthetic fertilizer than to straw application, and reducing synthetic fertilizer by 30% from the current condition is likely to be the most effective way to alleviate the effect of fertilization on GGWP. [ABSTRACT FROM AUTHOR]

The coastal saline-alkali soil in eastern China is an important reserve arable land resource. Adding exogenous organic material is an effective way to improve soil fertility and promote the conversion of saline-alkali soil to agricultural soil. In this study, a field plot experiment was used to investigate the influences of different organic materials (vinegar residue, VR; sewage sludge, SS; vermicompost, VC) on the reduction in salinity-alkalinity barrier factors, the accumulation of soil organic carbon (SOC), and the improvement in soil fertility in saline-alkali soil. The results indicated that applying different types of exogenous organic materials reduced soil electrical conductivity (EC) and pH, promoted SOC accumulation, and increased the barley yield. With the same application rate, VR application was more beneficial in reducing soil EC and pH, accumulating SOC, and increasing barley yield compared to SS and VC applications. In particular, the barley yield with VR application was higher than that with SS and VC applications by 18.4% and 26.6% on average, respectively, during the two-year experiment. Correlation and path analysis revealed that the barley yield was significantly negatively correlated with soil barrier factors (EC and pH), but EC in SS and VC-treated soils had an indirect negative effect on barley yield, while EC in VR-treated soil had a direct negative effect (−2.24). In addition, the direct (−4.46) and indirect (5.39) contributions of SOC to barley yield were higher with VR than those with SS and VC, while the direct contribution of soil aggregate to barley yield was lower with VR than that with SS and VC. Therefore, compared with SS and VC applications, VR application led to a fast reduction in soil barrier factors and the rapid accumulation of SOC, which were more beneficial for increasing barley yields in saline-alkali soil. [ABSTRACT FROM AUTHOR]