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• The soil respiration model used temperature, moisture, LAI, and soil properties as key variables. • The measured soil respiration in twenty-six agroecosystems in China well validated the model. • The modeled soil respiration varied greatly across regions and months in agroecosystems in China. Characterizing the seasonal patterns of soil respiration using a biophysical model is crucial for understanding the mechanisms of the terrestrial carbon (C) cycle. We developed a biophysical soil respiration model including four submodels that were associated with the functions of soil temperature, moisture, leaf area index (LAI), and soil organic C, and the model parameters were determined and calibrated. The modeling reproducibility was examined in various crop fields at our study site, and the modeling performance was also validated in twenty-five other agroecosystems in China. The monthly and annual soil respiration was estimated using the model, and the modeling uncertainty was evaluated. The results showed that the soil respiration model had good modeling reproducibility in various crop fields at our site. The measured soil respiration in the different agroecosystems in China validated our model well. The R 2, root mean squared error (RMSE), model efficiency (ME), and mean absolute error (MAE) were within the ranges of 0.449‒0.932, 0.526‒1.465 μmol m−2 s−1, 0.176‒0.796, and 0.312‒1.377 μmol m−2 s−1, respectively, across the twelve agroecosystem sites for validation. The measured soil respiration was significantly (P < 0.001) correlated with the modeled soil respiration across all twenty-six validation sites. Adding LAI significantly improved the modeling of soil respiration when the performance of the model (with LAI) vs. that without LAI was compared. The modeled monthly soil respiration was within the range of 0.016 ± 0.001‒0.117 ± 0.006 Pg C mo−1 in 2010 in agroecosystems in China. The black soils in Northeast China are fertile, which resulted in high temperature sensitivity and a clear increasing tendency of soil respiration from winter to summer in 2010 in agroecosystems in Northeast China. The established biophysical soil respiration model was proven to be effective for characterizing the temporal variations of soil respiration in various agroecosystems in China. [Display omitted] [ABSTRACT FROM AUTHOR]

Soil respiration ( R s ) is a key process in the terrestrial carbon cycle. Measurement and simulation of R s has received much attention recently. We collected annual R s field datasets to examine key controls of temporal and spatial variability in annual R s at the global scale. Published studies that reported annual field R s , mean annual temperature (MAT), annual precipitation (AP), soil (0–20 cm) properties and vegetation characteristics were compiled. MAT, AP and soil organic carbon (SOC) were the three most important variables in these datasets, together being responsible for 50% of the variance in annual R s in a global model (MAT&AP&SOC-model). Combining other site soil properties (e.g. pH) and vegetation variables, such as tree age (TA), tree height (TH), litter fall biomass (LF) and leaf area index (LAI), into the MAT&AP&SOC-model improved model performance. The site characteristic that explained the most variation in R s was AP followed by MAT, SOC, net primary productivity (NPP), pH, TA, TH, LF, LAI, elevation (EL) and diameter at breast height (DBH). Among the simulated models, the model based on MAT, AP, SOC and pH had the best fit for annual R s variance. There was a highly significant logarithmic relationship between R r / R s (the contribution of root respiration to R s ) and AP. The AP value of 0.4 m was a threshold for R r / R s , corresponding to R r / R s of 0.4 which reflects water limitation of root growth and plant productivity. [ABSTRACT FROM AUTHOR]

Abstract: To develop a model describing the dependence of annual soil respiration on climate and soil properties, we compiled 657 published annual soil respiration (R s ) measurements that were assembled from 147 sites globally, representing croplands, grasslands, forests and tundra ecosystems. Each of these annual soil respiration data was then aggregated with the appropriate mean air temperature (T) and annual precipitation (P) data derived from geographically referenced datasets and with soil properties gathered from the original literature. Partial correlation analyses showed that global annual R s significantly related to annual mean temperature, annual precipitation, and topsoil (0–20cm) organic carbon (SOC) storage, while topsoil total nitrogen (SN) and pH did not show a direct and clear relationship with R s across ecosystems. While we employed the T&P-model that used temperature and annual precipitation to globally predict annual soil respiration, it was able to explain 41%, 57%, and 31% of the variability of soil respiration for croplands, grasslands and forests, respectively. However, the residuals were significantly related to SOC for croplands and grasslands. Thus, we developed a T&P&C-model that includes SOC as an additional predictor of annual R s . This extended but still simple model performed better than the T&P-model and explained 69%, 89%, and 47% of the interannual and intersite variability of R s with a mean absolute error of 0.11, 0.18 and 0.28kgCm−2 yr−1 for croplands, grasslands and forests, respectively. Overall, the modeling efficiency of the T&P&C-model was nearly 60% across ecosystems. Globally, the mean turnover time of topsoil carbon (SOC/R s ) was highly comparable among croplands, grasslands and forests, equivalent to 6.1–6.3 years. Therefore, better estimates of global annual soil respiration would be obtained with the new model driven by climate and soil properties together. We expect significant improvements of global annual soil respiration predictions given that measurements of soil respiration coupling with soil properties and site productivities are widely taken across ecosystems over the world. [Copyright &y& Elsevier]

• CH 4 and N 2 O emissions from rice-aquaculture systems remain poorly constrained. • Rice-crayfish co-culture production is beneficial to reduce CH 4 and N 2 O emissions. • Ditched culture areas release more CH 4 but less N 2 O relative to rice planted areas. • Lower emission factors of N 2 O in rice-crayfish co-culture than monoculture systems. Rice paddies and rice-aquaculture co-culture systems are important sources of methane (CH 4) and nitrous oxide (N 2 O) to the atmosphere. Due to the growing human demand of aquaculture protein and the rapid development of green agriculture, rice-aquaculture co-culture systems have been increasingly developed from rice paddies in southeast China. However, the strength of CH 4 and N 2 O emissions from rice-aquaculture co-culture systems remains poorly quantified, in particular the contribution of these emissions from different functional cultivation areas. Here, a two-year parallel field experiment was conducted to examine the changes in CH 4 and N 2 O fluxes following the conversion from rice paddies to rice-crayfish co-culture systems in southeast China. Over the two-year period, annual CH 4 and N 2 O fluxes from rice-crayfish co-culture systems averaged 4.59 mg m-2 h-1 and 39.50 μg m -2 h -1, amounting to 391.9 kg ha−1 and 3.46 kg ha−1, respectively. The conversion from rice paddies to rice-crayfish co-culture systems significantly reduced CH 4 and N 2 O emissions by 14% and 31%, respectively. The emission factors of N 2 O were estimated to be 1.25–1.61% in the rice-crayfish co-culture system. Relative to rice planted areas, CH 4 and N 2 O fluxes from crayfish ditched culture areas were three times higher and 50% lower, which contributed 26% and 5% to the total CH 4 and N 2 O emissions from rice-crayfish co-culture systems, respectively. Our results highlight that the conversion of paddy rice monoculture to rice-crayfish co-culture systems can benefit low greenhouse gas emissions and high ecosystem economic profits as a potential option to sustain agricultural development and achieve carbon neutrality. [Display omitted] [ABSTRACT FROM AUTHOR]

• IndirectN2Oemissions from rivers draining agricultural watersheds are of increasing concerns. • Seasonal dissolved N2O concentrationsand N2O fluxes showed a similar variation pattern. • Annual mean model-based N2O fluxes werecomparable with chamber-based N2O fluxes. • The indirect riverine EFof N2O dependedlargely on surface water NO3–N concentrations. • Annual mean riverine indirect EF of N2O was significant lower than the IPCC default value. Indirect nitrous oxide (N 2 O) emissions from rivers draining agricultural watersheds are of increasing concerns due to riverine abundant sources of nitrogen loaded through leaching and runoff. However, the seasonal variation of N 2 O emissions from agricultural drainage rivers is poorly explored, especially the uncertainty in quantifying indirect N 2 O emission factors (EFs) from these aquatic environments. Here, a two-year study (2014-2016) was conducted to quantify indirect N 2 O emissions from a river draining a rice paddy watershed in subtropical China. Indirect N 2 O fluxes were simultaneously determined using the floating chamber method (chamber-based) and the gas exchange modeling approach (model-based) based on the measurement of dissolved N 2 O concentration. Results showed that seasonal dissolved N 2 O concentration and N 2 O fluxes had a similar variation pattern, with the highest and the lowest levels in summer and winter, respectively. The annual mean of model-based N 2 O fluxes (20.24 ± 3.34 μmol m−2 d−1) was generally in agreement with chamber-based N 2 O fluxes (18.70 ± 3.56 μmol m−2 d−1). The indirect emission factor of N 2 O was highly dependent on the surface water NO 3 −-N concentration. Annual mean indirect EF of N 2 O from the drainage river was estimated to be 0.00051, which was significantly lower than the default EF 5r value (0.0025) proposed by the Intergovernmental Panel on Climate Change (IPCC). These results suggest that the use of IPCC default value might have overestimated indirect N 2 O emissions from agricultural impacted riverine systems. Our study also highlights that more extensive in-situ measurements are required for monitoring indirect N 2 O emissions from agricultural impacted waters with different drainage characteristics, which would benefit for refining the IPCC EF 5r default value to further constrain global N 2 O budget. [ABSTRACT FROM AUTHOR]