Temperature dependence of greenhouse gas emissions from three hydromorphic soils at different groundwater levels

Exploring the ecological meanings of temperature sensitivity of ecosystem respiration from different methods

Temperature sensitivity (Q10) of ecosystem respiration (Re) is a critical parameter for predicting global terrestrial carbon dynamics and its response to climate warming. However, the determination of Q10 has been controversial. In this study, we scrutinized the underpinnings of three mainstream methods to reveal their relationships in estimating Q10 for Re in the Heihe River Basin, northwest China. Specifically, these methods are Q10 estimated from the long-term method (Q10_long), short-term method (Q10_short), and the low-frequency (Q10_lf) and high-frequency (Q10_hf) signals decomposed by the singular spectrum analysis (SSA) method. We found that: 1) Q10_lf and Q10_long are affected by the confounding effects caused by non-temperature factors, and are 1.8 ± 0.3 and 1.7 ± 0.3, respectively. 2) The high-frequency signals of the SSA method and short-term method have consistent roles in removing the confounding effects. Both Q10_short and Q10_hf reflect the actual response of respiration to temperature. 3) Overall, Q10_long has a larger variability (1.7 ± 0.3) across different biomes, whereas Q10_short and Q10_hf show convergence (1.4 ± 0.2 and 1.3 ± 0.1, respectively). These results highlight the fact that Q10 can be overestimated by the long-term method, whereas the short-term method and high-frequency signals decomposed by the SSA method can obtain closer and convergent values after removing the confounding effects driven by non-temperature factors. Therefore, it is recommended to use the Q10 value estimated by the short-term method or high-frequency signals decomposed by the SSA method to predict carbon dynamics and its response to global warming in Earth system models.

Assessing greenhouse gas emissions in Cuban agricultural soils: Implications for climate change and rice (Oryza sativa L.) production

Assessing the impact of greenhouse gas (GHG) emissions on agricultural soils is crucial for ensuring food production sustainability in the global effort to combat climate change. The present study delves to comprehensively assess GHG emissions in Cuba's agricultural soil and analyze its implications for rice production and climate change because of its rich agriculture cultivation tradition and diverse agro-ecological zones from the period of 1990-2022. In this research, based on Autoregressive Distributed Lag (ARDL) approach the empirical findings depicts that in short run, a positive and significant impact of 1.60 percent % in Cuba's rice production. The higher amount of atmospheric carbon dioxide (CO2) levels improves photosynthesis, and stimulates the growth of rice plants, resulting in greater grain yields. On the other hand, rice production index raising GHG emissions from agriculture by 0.35 % in the short run. Furthermore, a significant and positive impact on rice production is found in relation to the farm machinery i.e., 3.1 %. Conversely, an adverse and significant impact of land quality was observed on rice production i.e., -5.5 %. The reliability of models was confirmed by CUSUM and CUSUM square plot. Diagnostic tests ensure the absence of serial correlation and heteroscedasticity in the models. Additionally, the forecasting results are obtained from the three machine learning models i.e. feed forward neural network (FFNN), support vector machines (SVM) and adaptive boosting technique (Adaboost). Through the % MAPE criterion, it is evident that FFNN has achieved high precision (91 %). Based on the empirical findings, the study proposed the adoption of sustainable agricultural practices and incentives should be given to the farmers so that future generations inherit a world that is sustainable, and healthy.

Source partitioning and emission factor of nitrous oxide during warm and cold cropping seasons from an upland soil in South Korea

Nitrous oxide (N₂O) is a major greenhouse gas (GHG) with high global warming potential. A majority of the N₂O flux comes from agricultural sources, mainly due to nitrogen (N) fertilization. The soil N₂O flux, induced by N fertilization, mainly originated from two different sources, i.e., fertilizer and soil organic nitrogen (SON). It is essential to know the individual contribution of these two different sources in total N₂O flux for planning necessary mitigation strategies. It is also indispensable to know the seasonal difference of emission factors (EF) for having more accurate N₂O inventory. Therefore, an experiment was conducted in a South Korean upland soil during summer and winter seasons using ¹⁵N labeled urea as an artificial N source and source specific N₂O emissions were distinguished under different environmental conditions. To characterize the N₂O emissions from urea, 0, 50, 100 and 200% of the Korean N recommendation rate was selected for specified crops. The Korean N recommendation rate for red pepper (Capsicum annuum) and garlic (Allium sativum) was 190 and 250 kg N ha^-1, respectively. Direct emissions from urea were estimated from the difference of ¹⁵N₂O flux emitted from ¹⁵N-urea treated soil and the natural abundance of ¹⁵N₂O. From total N₂O fluxes, urea originated N₂O flux was 0.87% and 0.13% of the applied N in warm and cold seasons, respectively and the rest comes from SON. Nitrous oxide EF in the warm season was 2.69% of applied N and in the cold season that was 0.25%. Nitrous oxide fluxes showed a significant exponential relationship with soil temperature. The results show the necessity of considering the different N₂O EF for warm and cold cropping seasons to reduce uncertainty in N₂O inventory. The findings of this research may help better understand N₂O source partitioning and the emission budget from warm and cold cropping seasons.

Temperature response of denitrification rate and greenhouse gas production in agricultural river marginal wetland soils

Soils are predicted to exhibit significant feedback to global warming via the temperature response of greenhouse gas (GHG) production. However, the temperature response of hydromorphic wetland soils is complicated by confounding factors such as oxygen (O2 ), nitrate (NO3-) and soil carbon (C). We examined the effect of a temperature gradient (2-25 °C) on denitrification rates and net nitrous oxide (N2 O), methane (CH4 ) production and heterotrophic respiration in mineral (Eutric cambisol and Fluvisol) and organic (Histosol) soil types in a river marginal landscape of the Tamar catchment, Devon, UK, under non-flooded and flooded with enriched NO3- conditions. It was hypothesized that the temperature response is dependent on interactions with NO3--enriched flooding, and the physicochemical conditions of these soil types. Denitrification rate (mean, 746 ± 97.3 μg m(-2)  h(-1) ), net N2 O production (mean, 180 ± 26.6 μg m(-2)  h(-1) ) and net CH4 production (mean, 1065 ± 183 μg m(-2)  h(-1) ) were highest in the organic Histosol, with higher organic matter, ammonium and moisture, and lower NO3- concentrations. Heterotrophic respiration (mean, 127 ± 4.6 mg m(-2)  h(-1) ) was not significantly different between soil types and dominated total GHG (CO2 eq) production in all soil types. Generally, the temperature responses of denitrification rate and net N2 O production were exponential, whilst net CH4 production was unresponsive, possibly due to substrate limitation, and heterotrophic respiration was exponential but limited in summer at higher temperatures. Flooding with NO3- increased denitrification rate, net N2 O production and heterotrophic respiration, but a reduction in net CH4 production suggests inhibition of methanogenesis by NO3- or N2 O produced from denitrification. Implications for management and policy are that warming and flood events may promote microbial interactions in soil between distinct microbial communities and increase denitrification of excess NO3- with N2 O production contributing to no more than 50% of increases in total GHG production.