As one of the most important drivers of global climate change, land use change (LUC) has markedly altered the regional and global carbon (C) cycles. However, the geographic variations and the key drivers in the effects of LUC on temperature sensitivity (Q10) of soil microbial respiration (Rs) are still not fully elucidated, hence impeding the spatially explicit predictions of soil C cycling under climate change. Here, we used a paired-plot approach with data for 19 locations distributed from the tropical to temperate zones in eastern China, and compared the temperature responses of Rs between forest and cropland soil. Results showed that the latitudinal patterns of Q10 in forest soils were better explained by climatic variables; whereas in cropland, soil Q10 trended higher with increasing latitude, with climatic factors, pH, clay, and soil organic C (SOC) jointly modulating the spatial variations in Q10. Overall, the values of Q10 tended to converge with latitude between forests and croplands, with change in Q10 from forest to cropland, Delta Q10, significantly decreasing from the tropical region (9.23 +/- 3.58 %) to the subtropical (0.58 +/- 1.93 %) and temperate (-0.97 +/- 1.11 %) regions. Moreover, the spatial variations of Delta Q10 were significantly affected by climatic factors, Delta pH, Delta microbial biomass C (Delta MBC), and their interactions. Our findings highlight the potential impacts of LUC-related biogeographic variations in the temperature response of Rs, and emphasize the importance of incorporating the land-use effects on the temperature sensitivity of soil microbial respiration into terrestrial C cycle models to improve predictions of carbon-climate feedbacks in the future.

Soil carbon (C), nitrogen (N), and phosphorus (P) are required components to maintain ecosystem structure, function, and services. Accurate soil nutrient stoichiometry assessments are crucial for precisely managing agricultural and natural ecosystems. However, direct measurement and evaluation of soil characteristics can be costly and time-consuming. The development of statistical and machine learning-based methods for predicting soil C:N:P stoichiometry and microbial dynamics is of great significance. The objective of this study is to compare the performance of four machine learning models, i.e., support vector machine, random forest, extreme gradient boosting, and gradient boosting decision tree, in predicting soil C:N:P stoichiometry and net N mineralization rate and to evaluate their applicability to different agricultural land use types and climate zones. Our results showed that extreme gradient boosting (average R-2 > 0.81, RMSE <16.39, RPD > 2.67) and gradient boosting decision tree (average R-2 > 0.77, RMSE <16.40, RPD > 2.32) models performed the best in predicting C:N:P stoichiometry, demonstrating high accuracy and stability. Machine learning models produced higher accuracy in the vegetable field (except for C:N) than in the rice paddy field with average accuracy improvement of 42.9 %. The prediction performance in warm temperate and subtropical regions was inferior to cold regions. Feature importance assessment suggests that electrical conductivity, total N, and water-filled pore space may have significant predictive roles in the rice paddy field, while mean annual precipitation, total P, and silt content could be important factors in the vegetable field. When predicting the net N mineralization rate, soil texture may emerge as a crucial factor in the rice paddy field, whereas moisture content may play a key role in the vegetable field. Thus, machine learning models can be recommended to predict soil C:N:P stoichiometry and net N mineralization rate for precise agricultural practices.

Aims Nitrous oxide (N2O) plays an important role in global climate change. Plant expansion can alter soil nutrient cycling and greenhouse gas (GHG) fluxes but the effects of fine root litter mixing from plant expansion on litter decomposition, nutrient cycling, and GHG fluxes are poorly understood. Methods To evaluate the impacts of fine root litter mixing from plant expansion on GHG budgets, we conducted an in situ study over sixteen months in Japanese cedar (Cryptomeria japonica) plantations experiencing moso bamboo (Phyllostachys edulis) expansions. Litter decomposition and N2O production were quantified in a full-factorial complete-randomized design by comparing fine root litter of Japanese cedar, moso bamboo or their mixtures, with or without nitrogen (N) addition. Results We found that litter mixing accelerated decomposition, demonstrating a synergistic non-additive effect (19% increase in mass loss). This might have accelerated release of chemicals that inhibit microbial activities because litter mixtures had lower microbial biomass. Soil N2O emissions in the mixed litter treatment were 16.8% and 24.4% lower than those with only cedar or bamboo litter, respectively. N deposition promoted mass loss of mixed litter (28% increase in mass loss) and increased N2O emissions by 36.7%, 60.6% and 33.6% from cedar, mixed and bamboo litter treatments, respectively. Conclusion The expansion of moso bamboo promotes the return of soil nutrients, reduces N2O emissions, and potentially mitigates GHG emissions. However, with increasing N deposition in the future, moso bamboo expansion will likely contribute to global warming.

Temperature sensitivity (Q10) of soil organic matter (SOM) decomposition is an essential parameter that reflects the feedback relationship between climate warming and atmospheric CO2 concentration and plays a key role in accurately estimating changes in soil carbon pools and their feedback to climate change. Setting the incubation temperature range (ITR) scenario is essential to accurately estimate the temperature sensitivity of SOM decomposition; however, this has been widely ignored. To address this issue, we conducted a systematic incubation experiment using 54 soils covering the most typical ecosystems in China and nine ITR scenarios. The results showed that ITR scenarios had a significant effect on Q10 in different ecosystems and soil types. Combining the results of fitting mean annual temperature (MAT) to the Q10 of different ITR and the interpretation rate of factors (including climate, soil, and microbial community composition) to the Q10 of different ITR, the results indicate that the growing season average temperature range (GSA) scenario had better performance than the other ITR scenarios and should be the optimum ITR scenario. Furthermore, the variation of main influence factors of Q10 across different ecosystems should be accounted for to accurately predict the feedback between soil C cycle and climate change. Overall, our findings highlight the importance of the ITR for the estimates of Q10 using widespread experimental data, providing a reference for subsequent experiments accurately measure Q10, as well as compare and compile global data to better predict the feedback between the global carbon cycle and climate change.

Intensive management ofC.oleiferahas produced many pureC.oleiferaplantations. The transmission ofC.oleiferaplantation will potentially affect soil C, N, and P pools as well as their stoichiometric characteristics both in top soil layer and vertical soil profile due to the intensive management. To understand changes in vertical pools and stoichiometric characteristics of soil C, N, and P as affected by intensive management ofC.oleiferaplantations, both mixed and pureC.oleiferaplantations were studied. We conducted studies in five locations in Jiangxi, China with both pure and mixedC.oleiferaplantations, to compare changes in vertical pools and stoichiometry of C, N, and P. Both C and N pools were significantly different between mixed and pure plantation types ofC.oleifera. However, the ratio of C:N, C:P, and N:P was consistently higher in mixed plantations with C:P and N:P altered but C:N ratio did not change with soil depth. The intensive management significantly impact both C and N pools and the stoichiometry of C, N, and P. Intensive management ofC.oleiferaplantations decreased both C and N pools, especially at the depth of 30-50 cm soil layer.C.oleiferaplantation alteration from mixed to pure should be considered in future forest management practice considering the substantial effects on soil element cycling and distribution along vertical soil profile.

Research Highlights: This study examined the effect of mixing fine roots of Japanese cedar with moso bamboo on soil carbon dioxide (CO2) emissions with nitrogen (N) addition treatment. Background and Objectives: Moso bamboo expansion into adjacent forests and N deposition are common in subtropical China. The effects of litter input on soil CO2 emissions, especially fine root litter input, are crucial to evaluate contribution of moso bamboo expansion on greenhouse gas emissions. Materials and Methods: An in situ study over 12 months was conducted to examine mixing fine roots of Japanese cedar with moso bamboo on soil CO2 emissions with simulated N deposition. Results: Fine root litter input of Japanese cedar and moso bamboo both impacted soil CO2 emission rates, with mixed litter, positively impact soil CO2 emission rate with N addition treatment. Moso bamboo fine root litter input decreased the sensitivity of soil CO2 emission rate to soil temperature. Conclusions: The encroachment of moso bamboo into adjacent forests might benefit soil C sequestration under warming climate, which will also benefit the mitigation of global climate change.

Research Highlights: This study identifies the effect of nitrogen (N) and phosphorus (P) addition on stoichiometry correlations between understory plants and soil in subtropical Chinese fir plantations. Background and Objectives: Nitrogen and P are two nutrients limiting forest ecosystem production. To obtain more wood production, N and P are usually applied in plantation management. Changes in soil N and P will generally alter the stoichiometric characteristics of understory plants, which control carbon (C) and nutrient cycles between plants and soil. However, different correlations between plant and soil stoichiometry among functional groups of understory plants have not been investigated, which also impacted element cycling between plants and soil. Materials and Methods: Subtropical Chinese fir plantations were selected for N (100 kg ha−1 year−1) and P (50 kg ha−1 year−1) addition study. We collected fresh litter and the corresponding soil of four understory plants (Lophatherum gracile Brongn., Woodwardia japonica (L.f.) Sm., Dryopteris atrata (Kunze) Ching and Dicranopteris dichotoma (Thunb.) Berhn.) for study of C, N, and P stoichiometric ratios. Results: Nitrogen and P addition affected C, N, and P concentrations and stoichiometric ratios in litter and soil as well as correlations between litter and soil stoichiometric ratios. Understory plant species with different functional types impacted the correlations between plants and soil in C, N, and P stoichiometric ratios, especially correlations between litter C and soil C and N. Conclusions: Changes in soil N and P affect the stoichiometric ratios of understory plants. Functional groups impacted the correlation in C, N, and P stoichiometric ratios between plants and soil, indicating functional groups varied in their impacts on element cycling between plants and soil in plantations with exogenous nutrient addition, which should be considered in future management of plantations with intensive fertilization practice.