Abandoning cropland to enable recovery of the natural vegetation has been implemented during the past decade to restore the soil quality in the Loess Plateau, China. However, natural succession on cropland in the different slope aspect is different. The present study aimed at investigating the change in soil chemical and microbial properties abandoned farmland across time, and also to compare the difference of soil chemical and microbial properties in north slope and south slope on the Loess Plateau. The results showed that the slope aspect greatly affected the soil chemical and microbial properties after the cropland was abandoned, this could be attributed to the different precipitation and temperature in the two slopes. Compared with the north slope in which the organic C, total N and available N increased with the increase of abandonment years, that of south slope fluctuated significantly. Microbial biomass C and microbial biomass N in both slopes did not differ significantly in the first 10 years abandoned cropland, then decreased drastically in 15-year sites and thereafter tended to increase. Basal respiration in both slope fluctuated greatly in the sites with different abandonment years. Enzymes activities differed significantly in two slopes.
This data is a global meta-analysis dataset about the impact direction and intensity of nitrogen addition on plant-soil-enzyme C-N-P stoichiometry and microbial nutrient limitation of different ecosystem types (cropland, grassland, and forest), nitrogen addition intensity (0-5, 5-10, and >10 g N m-2 yr-1) and duration (0-5, 5-10, and >10yr).
In recent years, research on farmland soil stability has gained attention due to climate change. Studying the thermal stability of soil enzymes at key crop growth stages in response to increased CO2, drought, and warming is critical for evaluating climate change impacts on crop production and soil ecosystem stability. Despite its importance, research on the thermal stability of soil nutrient cycling enzymes remains limited. A pot experiment was conducted using the soil of winter wheat (Triticum aestivum L.), one of China’s main grain crops, as the research object. An artificial climate chamber was used to simulate four growth stages of winter wheat (jointing stage, flowering stage, grain filling stage, and maturity stage). Different levels of CO2 concentration (400 and 800 μmol mol−1), temperature conditions (current temperature and 4 °C higher), and water conditions (80% and 60% of field water capacity) were set, and their interactions were examined. By analyzing the temperature sensitivity (Q10) of soil enzyme activities related to soil carbon (C), nitrogen (N), and phosphorous (P) cycles in response to different treatments, the results showed that doubling CO2 concentration decreased soil C cycle enzyme Q10 and increased soil N and P cycle enzyme Q10 significantly. Additionally, soil C cycle enzyme Q10 decreased with increasing temperature, while other enzymes showed inconsistent responses. Mild drought significantly decreased the soil N-cycling enzyme Q10 in the early growth stage of winter wheat and the soil P-cycling enzyme Q10 in each growth stage, but significantly increased the soil N-cycling enzyme Q10 in the mature stage. The interaction between CO2 concentration doubling and warming exhibited a single-factor superimposed effect in reducing soil C cycle enzyme Q10. Moreover, doubling CO2 concentration offset the effect of mild drought stress on soil P cycle enzyme Q10. Above-ground biomass, soil total dissolved nitrogen, and nitrate nitrogen were identified as the primary factors influencing soil C, N, and P cycling enzyme Q10. This study is of great significance in exploring the effects of global warming on food production and the mechanism of soil ecosystem functional stability under future climate change.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)
